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Cardiovascular Disease Prediction using Data Mining Techniques
Abstract
Cardiovascular disease represents various diseases associated with heart, lymphatic system and circulatory system of human body. World Health Organisation (WHO) has reported that cardiovascular diseases have high mortality rate and high risk to cause various disabilities. Most prevalent causes for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, physical inactivity, ageing and addiction to drugs and alcohol are to name few. Factors such as hypertension, diabetes, hyperlipidemia, Stress and other ailments are at high risk to cardiovascular diseases. There have been different techniques to predict the prevalence of cardiovascular diseases in general and heart disease in particular from time to time by implementing variety of algorithms. Detection and management of cardiovascular diseases can be achieved by using computer based predictive tool in data mining. By implementing data mining based techniques there is scope for better and reliable prediction and diagnosis of heart diseases. In this study we studied various available techniques like decision Tree and its variants, Naive Bayes, Neural Networks, Support Vector Machine, Fuzzy Rules, Genetic Algorithms, and Ant Colony Optimization to name few. The observations illustrated that it is difficult to name a single machine learning algorithm for the diagnosis and prognosis of CVD. The study further contemplates on the behaviour, selection and number of factors required for efficient prediction.
... Shah et al. counted 2634 patents linking CVDs to the ECG signal [69]. Machine learning and data mining techniques [69][70] and deep learning [71] are widely used for automatic diagnosis based on the ECG signal. ...
Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.
... K. Gomathi et al [19] predicted heart disease using Naïve Bayes Classifier and J48 classifier. They have concluded that Naïve Bayes classifier reaches an accuracy of 79% where J48 classifier reaches an accuracy of 77%. ...
Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.
... K. Gomathi et al [19] predicted heart disease using Naïve Bayes Classifier and J48 classifier. They have concluded that Naïve Bayes classifier reaches an accuracy of 79% where J48 classifier reaches an accuracy of 77%. ...
... Heart disease refers to the heart disorder that result from infected vessels and blood clumps. In recent days, Heart Diseases are becoming the main cause of deaths ahead of cancer and road accidents in our society [1]. The reasons behind the improper functioning of circulatory system, constituted by heart and blood vessels, of human body includes sedentary life style, age, smoking, diabetes, lack of exercise, hypertension or high blood pressure, high cholesterol, obesity etc. Mortality rate is affected by heart diseases like heart valve disease, coronary artery diseases, arrhythmias [2,3]. ...
Heart Disease, one of the deadliest human diseases worldwide, should be properly diagnosed in time and treatments should be carried out accordingly. To predict Heart Diseases, decision making systems based on classification techniques have been widely proposed in various studies. In this paper, an Integrated Decision Making System (IDMS) has been introduced for prediction of heart disease. In addition, it uses Principal Component Analysis (PCA) for dimensionality reduction, Agglomerative hierarchical clustering technique for clustering and Random Forest (RF) for classification purpose. Then, the results are compared with other six conventional classification techniques. Some experiments are performed using Cleveland Heart Disease Dataset (CHDD) sourced from UCI-ML repository and Python language concluding that the proposed system provides better results comparing with other conventional methods. The proposed integrated decision making system will help out the doctors to diagnose the heart patients professionally and it may be useful for further investigation and predictions using different datasets and resulting valuable knowledge on Heart Disease.
... K. Gomathi et al in 4 implemented Naïve Bayes (NB) and J48 classifier and predicted heart disease. Their results concluded that Naïve Bayes classifier reaches an accuracy of 79% where J48 classifier reaches an accuracy of 77%. ...
Cardiovascular Disease (CVD) affects the walls of the arteries thatsupply the myocardium. CVD is not a single disease. It is a cluster ofdiseases and injuries that affect the cardiovascular system i.e. the heartand blood vessels. Heart failure is concern in patients with COVID-19.Alternately mental anxiety during lockdown period of COVID-19 mayincrease CVD. For assisting health care system, an automated tool isproposed in this paper that uses Reccurrent Neural Network (RNN).A stacked bi-directional LSTM layer based model is proposed in thispaper that considers interfering factors from past health records whiledetecting patients with cardiac problems. Experimental results haveindicated promising accuracy of 93.22% in predicting patients withcardiac troubles.
... Kirmani in cardiovascular disease prediction using data mining techniques [2], explored different approaches of disease prediction with various scientific methods like neural network, Naïve Bayes, support vector machine and decision tree based classification and disease prediction algorithms. Finally it has been concluded that it is hard to find the cardio vascular disease CVD. ...
The problem of classification in medical data has been well studied and there exist number of solution for the classification of medical using different measures and methods. However, the methods still suffer to achieve higher performance in classification accuracy. Towards the development of classification performance, the Multi Level Incremental Influence Measure (MLIIM) based classification algorithm is presented in this paper. The method preprocess the input data set to fix the noise issue by removing the incomplete data. In the second stage, the method estimates the influence measure in multiple levels at iterative manner. Finally, the method estimates, class influence weight (CIW) for different classes. Based on the computed class influence weight, an target class is selected to assign label to the data point. The proposed algorithm produces efficient classification and increases the classification accuracy.
... In Kirmani (2017), the author discusses various data mining techniques towards cardiovascular disease prediction. The author reviewed different methods like SVM, NB, DT, GA and ANT methods in disease prediction. ...
The association rule based classification is imperative in the disease prediction owing to its high predictability. To deal with the sensitive data, we propose an algorithm using fuzzy inference set. The association rule mining is improved further by generating an associative rules for each item of the data set. The ranking of the item in the data set is based on the information mass value estimated. The mass value represents the depth of the item in the data set and its class. Selection of the certain item set is done based on the mass value of different associated items. According to the associative items selected, the association rule mining is performed. For each association rule generated, this method calculates the impact of each object from the rules based on how fuzzy rules are generated. Fuzzy impact rules indicate symptoms and diagnostic labels. A class of disease posses disease influence measure that predicts each class of disease has changed. The proposed algorithm improves the classification efficiency and reduces the error rates.
... Cardio-vascular diseases relate to the heart and vessels. They represent today in our society the main cause of deaths before cancers and road accidents [3]. ...
The risk of developing early heart disease is always prominent. In fact, according to the Central of Disease Control and Prevention (CDC), Cardiovascular disease accounts for nearly 801,000 deaths in the US.
In this paper, we present a machine learning and decision based system for heart disease prediction.
We validate our approach on the Heart Disease Dataset gathered from the UCI machine learning repository.
Cleveland, Hungarian and Switzerland datasets are combined and trained with the use of SVM and Naïve Bayes algorithms.
Comparison and analysis with other models show that the accuracy of the proposed approach is 87% and 86% for SVM and Naïve Bayes respectively, which is much higher comparing to the existing approaches that use a small subset of data and no imputation technique.
... By testing and training phase a certain parameter, it provides 86.3% accuracy in testing phase and 87.3% in training phase. (Kirmani, 2017) suggested multi disease prediction using data mining techniques. Nowadays, data mining plays vital role in predicting multiple disease. ...
... Kirmani in cardiovascular disease prediction using data mining techniques [2], explored different approaches of disease prediction with various scientific methods like neural network, Naïve Bayes, support vector machine and decision tree based classification and disease prediction algorithms. Finally it has been concluded that it is hard to find the cardio vascular disease CVD. ...
The problem of classification in medical data has been well studied and there exist number of solution for the classification of medical using different measures and methods. However, the methods still suffer to achieve higher performance in classification accuracy. Towards the development of classification performance, the Multi Level Incremental Influence Measure (MLIIM) based classification algorithm is presented in this paper. The method preprocess the input data set to fix the noise issue by removing the incomplete data. In the second stage, the method estimates the influence measure in multiple levels at iterative manner. Finally, the method estimates, class influence weight (CIW) for different classes. Based on the computed class influence weight, an target class is selected to assign label to the data point. The proposed algorithm produces efficient classification and increases the classification accuracy.
Genius hour is a movement that allows students to discover their interests and encourages
creativity in the classroom environment. The roots of the genius hour are based on the fact that
international technology companies leave 20 percent of their working time free to work in their
areas of interest to increase the creativity of their employees.
Employees develop various projects in their areas of interest and interests. Later, it was adapted
to the classroom environment by Juliani as inquiry-based and project-based learning. Unlike
project-based learning, students need to discover their inner world and interests rather than
academic gain. Students who discover their interests also have the opportunity to discover their
learning styles. It contributes to the development of many other skills as well as the ability to
think scientifically and prepare projects.
In the process, what students work, how they work, and what they produce or create as a result
is controlled by the teacher. Teachers, who are in a guiding position, encourage the development
of creativity and self-learning in the work that students will do within their interests. Here, the
guidance of the teachers is mainly based on student interests, background information, and
learning interests. When the implementation process is examined, teachers allocate one lesson
or one hour per week.
On the other hand, they teach with a distribution where 80 percent of the time is based on
traditional standards and 20 percent is student-focused. In both forms of practice, students have
the opportunity to research areas of interest and curiosity. In science education, students must
learn by researching the topics that are of interest to them according to their learning style.
Especially, each of the questions that occur in the minds of the students during the lesson can
be a Genius hour project to improve the science education lessons. For example, considering a
solar system and beyond unit, students can be expected to develop different interests and
different projects.
Data mining is one of the essential areas of research that is more popular in health organization. Heart disease is the leading cause of death in the world over the past 10 years. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. This research intends to provide a detailed description of Naïve Bayes, decision tree classifier and Selective Bayesian classifier that are applied in our research particularly in the prediction of Heart Disease. It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on Cleveland datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on this dataset of which C4.5 outperform NB. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classifier and experiment also reveals that selective Bayesian classifier has a better accuracy as compared to other classifiers.
Data mining have great potential for healthcare industry to enable health systems to systematically use data and identify the efficiency and improve care with reduce cost. Due to complexity of healthcare and slower improvement of technology adoption needs to implement effective data mining. In data mining there are variety of techniques and methods to suggest or decision making knowledge in database and extracting or support prediction of data. With growing research on heart disease prediction system, this research work has developed a Heart disease prediction with comparative study of several data mining algorithms. Many data mining prediction tools are there in worldwide to make prediction for medical data. In this study it is observed that compare several algorithms to analysis the performance, accuracy of the heart disease prediction system. The commonly used techniques of heart disease prediction and their analytics, summaries are summarized in this research work.
Nowadays, the healthcare sector is one of the areas where huge data are daily generated. However, most of the generated data are not properly exploited. Important encapsulated data are currently in the data sets. Therefore, the encapsulated data can be analyzed and put into useful data. Data mining is a very challenging task for the researchers to make diseases prediction from the huge medical databases. To succeed in dealing with this issue, researchers apply data mining techniques such as classification, clustering, association rules and so on. The main objective of this research is to predict heart diseases by the use of classification algorithms namely Naïve Bayes and Support Vector Machine in order to compare them on the basis of the performance factors i.e. probabilities and classification accuracy. In this paper, we also developed a computer-based clinical Decision support system that can assist medical professionals to predict heart disease status based on the clinical data of the patients using Naïve Bayes Algorithm. It is a web-based user-friendly system implemented on ASP.NET platform with C# and python for the data analysis. From the experimental results, it is observed that the performance of Naïve Bayes is better than the other Algorithm.
The successful application of data mining in highly visible fields like e-business, marketing and
retail has led to its application in other industries and sectors. Among these sectors just
discovering is healthcare. The Healthcare industry is generally “information rich”, but
unfortunately not all the data are mined which is required for discovering hidden patterns &
effective decision making .Discovery of hidden patterns and relationships often goes
unexploited. Advanced data mining modeling techniques can help remedy this situation.
This research paper intends to use data mining Classification Modeling Techniques, namely,
Decision Trees, Naïve Bayes and Neural Network, along with weighted association Apriori
algorithm and MAFIA algorithm in Heart Disease Prediction. Using medical profiles such as
age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting heart
disease.
research on causes of death due to heart disease has shown that it is the number one cause of death. If current trends are allowed to continue, 23.6 million people will die from heart disease in coming 2030. The healthcare industry gathers enormous amounts of heart disease data which unfortunately, are not "mined" to discover hidden information for effective decision making. In this paper, study of PCA has been done which finds the minimum number of attributes required to increase the accuracy of various supervised machine learning algorithms. The objective of this research is to analyze supervised machine learning algorithms to predict heart disease.
Data mining techniques have been applied magnificently in many fields including business, science, the web, cheminformatics, bioinformatics and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relatiomhps and trends in medical data obtained from clinical records. Ths study reviews the state of the art research on heart disease diagnosis and prediction.
Data mining technology provides a user- orientedapproach to novel and hidden patterns in the data anddiscovering association rules in medical data to predict heartdisease. mapping medical data to a transaction format suitablefor mining association rules and identifying useful constraints.However, there is a lack of effective analysis tools to discoverhidden relationships and trends in data. Knowledge discoveryand data mining have found numerous applications in businessand scientific domain
Computational intelligence combines fuzzy systems, neural network and evolutionary computing. In this paper, Neuro-fuzzy integrated system for coronary heart disease is presented. In order to show the effectiveness of the proposed system, Simulation for automated diagnosis is performed by using the realistic causes of coronary heart disease. The results suggest that this kind of hybrid system is suitable for the identification of patients with high/low cardiac risk.
Associative classification is a recent and rewarding technique which
integrates association rule mining and classification to a model for
prediction and achieves maximum accuracy. Associative classifiers are
especially fit to applications where maximum accuracy is desired to a
model for prediction. There are many domains such as medical where the
maximum accuracy of the model is desired. Heart disease is a single
largest cause of death in developed countries and one of the main
contributors to disease burden in developing countries. Mortality data
from the registrar general of India shows that heart disease are a major
cause of death in India, and in Andhra Pradesh coronary heart disease
cause about 30%of deaths in rural areas. Hence there is a need to
develop a decision support system for predicting heart disease of a
patient. In this paper we propose efficient associative classification
algorithm using genetic approach for heart disease prediction. The main
motivation for using genetic algorithm in the discovery of high level
prediction rules is that the discovered rules are highly comprehensible,
having high predictive accuracy and of high interestingness values.
Experimental Results show that most of the classifier rules help in the
best prediction of heart disease which even helps doctors in their
diagnosis decisions.
Objectives: The objective of our work is to analyse various data mining tools and techniques in health care domain that can be employed in prediction of heart disease system and their efficient diagnosis. Methods/Statistical Analysis: A heart disease prediction model, which implements data mining technique, can help the medical practitioners in detecting the heart disease status based on the patient's clinical data. Data mining classification techniques for good decision making in the field of health care addressed are namely Decision trees, Naive Bayes, Neural Networks and Support Vector Machines. Hybridizing or combining any of these algorithms helps to make decisions quicker and more precise. Findings: Data mining is a powerful new technology for the extraction of hidden predictive and actionable information from large databases that can be used to gain deep and novel insights. Using advanced data mining techniques to excavate valuable information, has been considered as an activist approach to improve the quality and accuracy of healthcare service while lowering the healthcare cost and diagnosis time. Using this technique presence of heart disease can be predicted accurately. Using more input attributes such as controllable and uncontrollable risk factors, more accurate results could be achieved. Applications/Improvements: This method can be further expanded. It can use many of input attributes. Other data mining techniques are also be used for predication such as Clustering, Time series, Association rules. The unstructured data available in healthcare industry database can also be mined using text mining.
Heart syndrome is a most important reason of mortality and death for people in most of the countries all over the world. There are many factors of heart disease that affecting the structure or function of the heart. These might be challenging for doctors to predict heart disease accurately. Therefore, it is important to utilize automated innovations in coronary illness conclusion to help specialists to determine quicker to have higher precisely. Researchers have developed many techniques such as data mining, soft computing and optimization techniques for diagnosing heart disease. In this paper, we present a survey paper on the use of swarm intelligence for the detection of heart disease. This survey helps to understand the different swarm intelligence techniques that are involved in diagnosis for heart disease. This paper is relevant to swarm intelligent techniques which are involved in prediction of heart disease. © 2016, International Journal Of Pharmacy and Technology. All rights reserved.
Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Naive Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes.
The diagnosis of heart disease which depends in most cases on complex grouping of clinical and pathological data. Due to this complexity, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart diseaseprediction. In case of heart disease, the correct diagnosis in early stage is important as time is very crucial. Numeral number of tests must be requisite from the patient for detecting a disease. Machine learning based method is used to classify between healthy people and people with disease. Cardiovascular disease is the principal source of deaths widespread and the prediction of Heart Disease is significant at an untimely phase. In order to reduce number of deaths from heart diseases there has to be a quick and efficient detection technique. This work presents a comprehensive review for the prediction of cardiovascular disease by using machine learning based approaches.
The diagnosis of diseases is a vital and intricate job in medicine. The recognition of heart disease from diverse features or signs is a multi-layered problem that is not free from false assumptions and is frequently accompanied by impulsive effects. Thus the attempt to exploit knowledge and experience of several specialists and clinical screening data of patients composed in databases to assist the diagnosis procedure is regarded as a valuable option. This research work is the extension of our previous research with intelligent and effective heart attack prediction system using neural network. A proficient methodology for the extraction of significant patterns from the heart disease warehouses for heart attack prediction has been presented. Initially, the data warehouse is pre-processed in order to make it suitable for the mining process. Once the preprocessing gets over, the heart disease warehouse is clustered with the aid of the K-means clustering algorithm, which will extract the data appropriate to heart attack from the warehouse. Consequently the frequent patterns applicable to heart disease are mined with the aid of the MAFIA algorithm from the data extracted. In addition, the patterns vital to heart attack prediction are selected on basis of the computed significant weightage. The neural network is trained with the selected significant patterns for the effective prediction of heart attack. We have employed the Multi-layer Perceptron Neural Network with Back-propagation as the training algorithm. The results thus obtained have illustrated that the designed prediction system is capable of predicting the heart attack effectively.
The healthcare industry collects large amounts of healthcare information which cannot be mined to find unknown information for efficient evaluation. Discovery of buried patterns frequently goes unexploited. Heart disease is a term for defining a huge amount of healthcare conditions that are related to the heart. This medicinal condition defines the unpredicted health conditions that directly control all the parts of the heart. Different data mining techniques such as association rule mining, classification, clustering are used to predict the heart disease in health care industry. The heart disease database is preprocessed to make the mining process more efficient. The preprocessed data is clustered using clustering algorithms like K-means to cluster relevant data in database. Maximal Frequent Item set Algorithm (MAFIA) is used for mining maximal frequent patterns in heart disease database. The frequent patterns can be classified using C4.5 algorithm as training algorithm using the concept of information entropy. The results showed that the designed prediction system is capable of predicting the heart attack successfully.
Comparison of classification techniques in Data mining to find the best technique for creating risk prediction model of heart disease at minimum effort. In Data mining, different methods used to find risk prediction of heart disease. There are two types of model used in analysis of data. First one is applying single model to various heart data and another one is applying combined model to the data. The combined model also known as hybrid model. This paper provides a quick and easy understanding of various prediction models in data mining and helps to find best model for further work. This is unique approach because various techniques listed and expressed in bar chart to understand accuracy level of each. These techniques are chosen based on their efficiency in the literature. In previous studies of different researcher expressed their effort on finding best approach for risk prediction model and here we found best model by comparing those researcher’s findings as survey. This survey helps to understand the recent techniques involved in risk prediction of heart disease at classification in data mining. Survey of relevant data mining techniques which are involved in risk prediction of heart disease provides best prediction model as hybrid approach comparing with single model approach.
Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour(KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.
The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still "information rich" but "knowledge poor". There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today"s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.
Summary The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not "mined" to determine concealed information for effective decision making by healthcare practitioners. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the heart disease warehouses for heart attack prediction. Initially, the data warehouse is preprocessed to make it appropriate for the mining process. After preprocessing, the heart disease warehouse is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the warehouse. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA algorithm. Then the significant weightage of the frequent patterns are calculated. Further, the patterns significant to heart attack prediction are chosen based on the calculated significant weightage. These significant patterns can be used in the development of heart attack prediction system.
Prediction of Heart Disease using Data Mining Techniques
- Era Singh Kajal
- Ms Nishika
Era Singh Kajal, Ms. Nishika. Prediction of
Heart Disease using Data Mining Techniques.
www.Ijariit.com
26.
Lakshmi Devasena, PERFORMANCE
EVALUATION OF MEMORY BASED
CLASSIFIERS WITH CORRELATION
BASED FEATURE SELECTION SUBSET
EVALUATOR FOR SMART HEART DISEASE
PREDICTION. IJRET: Volume: 05 Special
Issue: 07 | NCEITCS-2016.
An Efficient Classification Tree Technique for Heart Disease Prediction
- S Vijiyarani
S. Vijiyarani et. al., "An Efficient Classification
Tree Technique for Heart Disease Prediction"
Proceedings published in International
Journal of Computer Applications, 2013.
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction
- Nidhi Bhatla
- Kiran Jyoti
Nidhi Bhatla, Kiran Jyoti. An Analysis of Heart
Disease Prediction using Different Data
Mining techniques. International Journal of
Engineering Research & Technology
34.
Jyoti Soni Ujma Ansari Dipesh Sharma,
Sunita Soni. "Predictive Data Mining for
Medical Diagnosis: An Overview of Heart
Disease Prediction".
Hybridizationof Improved K-Means and Artificial Neural Network for Heart Disease Prediction
- Dr Shubhada Bhalerao
- Baisa
- Gunjal
Shubhada Bhalerao, Dr. Baisa Gunjal,
Hybridizationof Improved K-Means and
Artificial Neural Network for Heart Disease
Prediction. IJCST-Volume 4, Issue 3, May
-Jun 2016.
Heart Disease Prediction Using ANN and Improved K-Means
- R Ankita
- Madhuri N Mokashi
- Pooja T Tambe
- Walke
Ankita R. Mokashi, Madhuri N. Tambe, Pooja
T. Walke. Heart Disease Prediction Using
ANN and Improved K-Means. International
Journal of Innovative Research in Electrical,
Electronics, Instrumentation and Control
Engineering Vol. 4, Issue 4, April 2016.
Different Data Mining Approaches for Predicting Heart Disease
- Ashwini Shetty
- Chandra Naik
Ashwini Shetty, Chandra Naik, Different Data
Mining Approaches for Predicting Heart
Disease. International Journal of Innovative
Research in Science, Engineering and
Technology Vol. 5, Special Issue 9, May
2016.
Comparative Analysis Of Support Vector Machine Ensembles For Heart Disease Prediction”
- V Subha
- M Revathi
- D Murugan
Survey on Data Mining Techniques for The Diagnosis of Diseases In Medical Domain
- I Parvathi
- Siddharth Rautaray
Study of Heart Disease Prediction using Data Mining
- K Sudhakar
- . M Dr
- Manimekalai
Classification Model for the Heart disease Diagnosis
- Atul Kumar Pandey
- Prabhat Pandey
Heart Disease Analysis System Using Data Mining Techniques
- G Karthiga
- C Preethi
- R Delshi Howsalya
Genetic Neural Approach for Heart Disease Prediction
- . P Nilakshi
- Waghulde
- . P Nilima
- Patil
Diagnosis of Heart Disease Using Data Mining Algorithm
- Deepali Chandna
Computer-Based Clinical Decision Support System for Prediction of Heart Diseases Using Naïve Bayes Algorithm
- D Ratnam
- V Himabindu
- S P Sai
- P Raghavendra Rama Devi
- Rao
An Efficient Data Mining and Ant Colony Optimization technique (DMACO) for Heart Disease Prediction
- Animesh Dubey
- Rajendra Patel
- Khyati Choure
Analysis Of Data Mining Techniques For Diagnosing Heart Disease”
- Jyoti Rohilla
- Preeti Gulia
Analysis Of Heart Attack Prediction System Using Genetic Algorithm”
- Dr Williamjeet Beant Kaur
- Singh
A Proficient Heart Disease Prediction Method Using Different Data Mining Tools
- K Manimekalai
Data Mining Techniques For Heart Disease Prediction
- Aswathy Wilson
- Jismi Simon
- Liya Thomas
- Soniya Joseph
Heart Disease Diagnosis Using Predictive Data mining
- B Venkatalakshmi
- M Shivsankar
Chinna Rao “Decision Support in heart disease prediction system using naïve bays
- . G Mrs
- Mr M Subbalakshmi
Review on Heart Disease Diagnosis Based on Data Mining Techniques
- Punam Bajaj
- Preeti Gupta
Heart Disease Prediction System using Data Mining Clustering Techniques
- Meenu Singla
- Kawaljeet Singh