Machine Learning Techniques for Precise Heart Disease Prediction
Abstract
Diagnosing and forecasting cardiovascular disease represents a pivotal task in medicine, crucial for accurately categorizing
and effectively treating patients under the care of cardiologists. Within the medical domain, the integration of machine learning has grown, offering the capability to identify patterns from extensive datasets. Employing machine learning for the classification of cardiovascular disease occurrences holds promise in reducing diagnostic errors. This study introduces a novel method using k-modes clustering with Huang initialization to enhance the precision of classification. Various models, including random forest (RF), decision tree (DT), multilayer perceptron (MLP), and XGBoost (XGB), were employed and their parameters optimized using GridSearchCV.
Evaluation was conducted on a practical dataset comprising 70,000 instances sourced from Kaggle, yielding the following accuracies: decision tree: 86.37% (with cross-validation) and 86.53% (without), XGBoost: 86.87% (with) and 87.02% (without), random forest: 87.05% (with) and 86.92% (without), multilayer perceptron: 87.28% (with) and 86.94% (without). Additionally, these models demonstrated robust AUC values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The study concludes that the multilayer perceptron model, particularly with cross-validation, exhibited superior performance with the highest accuracy of 87.28%.
... According to Saini (2023), Machine learning algorithms are gradually revolutionizing heart disease prediction since they can handle complex, multi-dimensional data sets. Traditional risk models-hardwired with a limited set of variables such as age, cholesterol levels, blood pressure, and smoking status-have been used until now for estimates regarding the possibility of developing heart diseases for a specific individual. ...
Heart disease persists as one of the leading causes of death in the USA and worldwide, accounting for a substantial proportion of global mortality. The significance of early detection of heart disease lies in its capability to counter catastrophic events such as strokes and heart attacks, which are often irreversible and fatal. Machine learning algorithms are gradually revolutionizing heart disease prediction since they can handle complex, multi-dimensional data sets. This research project used the Cleveland dataset from the UCI Machine Learning Repository, containing 70,000 records of patients with 12 unique features. Three machining learning algorithms were trained: Logistic Regression, Random Forest, and Support Vector Machines. Each algorithm was evaluated for precision, accuracy, recall, F1-score, and ROC-AUC. Based on the proof of the evaluation metrics for Logistic Regression, Random Forest, and SVM. In that respect, Logistic Regression was the best overall model since it yielded the highest ROC-AUC score, balancing true positives and false positives better than the rest of the models. The Support Vector Machine had the best accuracy, although it performed similarly to Logistic Regression but slightly lower. In retrospect, the implications for heart disease prediction are evident with simple algorithms such as Logistic Regression affirmatively performing better in specific early heart detection tasks, especially when balancing precision and recall. Indisputably, Machine learning models will have a high clinical impact on heart disease prediction since they enable early detection of heart diseases, leading to timely interventions and better patient prognoses.
Cardiovascular diseases (CVDs), which include conditions such as coronary artery disease, heart failure, and stroke, remain the leading cause of death worldwide, accounting for a significant proportion of global mortality. The growing prevalence of CVDs highlights the critical need for early detection and accurate diagnosis to improve patient outcomes and reduce healthcare burdens. Traditional diagnostic tools, such as electrocardiograms (ECGs), echocardiograms, and imaging techniques like MRI and CT scans, are widely used to assess cardiovascular health. However, these methods are often hindered by limitations in speed, accuracy, and scalability, particularly in settings with limited resources. In recent years, the advent of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has transformed the landscape of medical diagnostics, offering new opportunities for enhancing CVD detection. AI algorithms, trained on vast datasets, can recognize complex patterns in clinical records, ECG signals, medical imaging, and even genetic data, enabling the identification of cardiovascular conditions with unprecedented precision. This capability can assist clinicians in detecting subtle abnormalities that may otherwise go unnoticed by traditional methods, leading to earlier and more accurate diagnoses. Moreover, AI's ability to analyze large volumes of data in real-time holds potential for improving workflow efficiency in busy clinical environments. However, the integration of AI in CVD detection is not without its challenges. One of the primary concerns is the quality of data used to train AI models, as inaccurate or incomplete data can lead to misleading results. Another obstacle is the interpretability of AI models, as deep learning algorithms, in particular, often operate as "black boxes," making it difficult for clinicians to understand the rationale behind a model's predictions. This lack of transparency can hinder trust and clinical adoption. Additionally, the implementation of AI-based diagnostic tools requires careful consideration of regulatory standards and ethical implications, such as ensuring patient privacy and addressing potential biases in AI models. To address these challenges, significant progress has been made in the field of explainable AI (XAI), which focuses on developing models that provide interpretable results while maintaining high levels of accuracy. Explainable AI can help clinicians better understand how AI models make predictions, thereby enhancing trust and facilitating more informed decision-making. Furthermore, the integration of AI with wearable technologies, such as smartwatches and fitness trackers, has opened new avenues for continuous monitoring of cardiovascular health. Wearable devices, coupled with AI algorithms, can provide real-time insights into heart rate, blood pressure, and other vital signs, enabling personalized care and early detection of potential cardiovascular events. This article explores the role of AI in the detection and diagnosis of CVDs, highlighting the potential benefits such as improved accuracy, speed, and scalability, as well as the challenges related to data quality, interpretability, and clinical adoption. It also examines the ongoing advancements in explainable AI and the integration of wearable technologies, which offer promising pathways for personalized and real-time cardiovascular care. Finally, the article discusses future directions for research and clinical implementation, emphasizing the importance of collaboration between AI researchers, clinicians, and healthcare providers to ensure the successful integration of AI into routine cardiovascular care and the broader healthcare system.
Cardiovascular diseases (CVDs) are the leading cause of global mortality, emphasizing the importance of early and accurate detection. While traditional diagnostic methods remain useful, they are often limited by speed, accuracy, and scalability. The emergence of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has revolutionized the ability to analyze complex medical data. By integrating these tools, clinicians can better interpret clinical records, ECG signals, and medical imaging to identify CVDs with unprecedented precision. Despite challenges like data quality, interpretability, and clinical adoption, advancements in explainable AI (XAI) and wearable technology integration offer promising pathways for personalized and real-time care. This article examines AI's role in CVD detection, its benefits, challenges, and the future directions for research and clinical implementation.
Cardiovascular diseases (CVDs) remain the world's leading cause of mortality, underscoring the need for advanced methods of early detection and accurate diagnosis. Traditional diagnostic techniques, while effective, are often limited in their ability to process and interpret large and complex datasets. The advent of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has opened new frontiers in cardiovascular diagnostics. These technologies analyze diverse data types, such as clinical records, ECG signals, and medical imaging, enabling unprecedented precision in identifying early signs of CVDs. Despite hurdles like data quality, model interpretability, and integration challenges, emerging solutions in explainable AI (XAI) and wearable technologies offer transformative potential. This article explores AI's applications in CVD detection, addresses challenges, and highlights future opportunities for innovation and implementation.
Early detection of blood cancers such as leukemia, lymphoma, and myeloma significantly improve patient outcomes. Traditional diagnostic approaches often rely on manual interpretation, which can be time-consuming and subject to human error. Advances in machine learning (ML) and artificial intelligence (AI) offer transformative potential in the automation and enhancement of diagnostic procedures. This article examines various ML classification algorithms, including decision trees, support vector machines (SVM), neural networks, random forests, K-nearest neighbors (KNN), and logistic regression, for diagnosing blood cancers. It explores their strengths, limitations, and practical applications, focusing on clinical, imaging, and genetic data. Furthermore, it discusses challenges such as data quality, interpretability, and ethical considerations, and highlights promising research directions, including multi-modal data integration and explainable AI, to pave the way for personalized and accessible cancer diagnostics.
The early detection of blood cancers, including leukemia, lymphoma, and myeloma, plays a critical role in improving patient outcomes. Traditional diagnostic methods, however, are often slow and rely on the expertise of clinicians, creating a need for more efficient, accurate, and automated approaches. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in medical diagnostics, particularly in cancer detection. This article explores the application of various classification techniques-such as decision trees, support vector machines (SVM), neural networks, random forests, K-nearest neighbors (KNN), and logistic regression-in diagnosing blood cancers. It delves into the strengths, limitations, and practical applications of these methods, emphasizing their potential to analyze clinical data, medical imaging, and genetic information. Additionally, the article addresses key challenges like data quality, model interpretability, and ethical concerns surrounding the use of AI in healthcare. It also highlights current real-world applications and case studies that demonstrate the transformative potential of machine learning in blood cancer diagnosis. Finally, the article outlines future research directions, including the integration of multi-modal data and advancements in explainable AI, which could lead to more personalized, accurate, and accessible diagnostic tools for blood cancer.
Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention critical to improving patient outcomes. Machine learning techniques, particularly Support Vector Machine (SVM) and Artificial Neural Networks (ANN), have emerged as powerful tools for predicting heart disease risk based on clinical data. This study explores the effectiveness of SVM and ANN in heart disease prediction, utilizing datasets containing key risk factors such as age, blood pressure, cholesterol levels, and other health indicators. The research involves data preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that both models can achieve high accuracy in predicting heart disease, with SVM excelling in simpler, lower-dimensional datasets, and ANN showing superior performance with larger, more complex datasets. A comparative analysis of the two models highlights their respective strengths and weaknesses, providing insights into their suitability for different clinical scenarios. The findings suggest that both SVM and ANN have significant potential to aid in the early detection of heart disease, contributing to better clinical decision-making and personalized treatment plans. Future work will focus on enhancing model interpretability, integrating additional machine learning algorithms, and exploring real-world clinical applications.
Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating accurate and timely predictive models to aid in early diagnosis and intervention. Machine learning (ML) has emerged as a powerful tool in healthcare, offering the potential to enhance heart disease prediction. Among various ML techniques, ensemble learning-combining multiple individual models to improve performance-has shown significant promise in medical applications. This paper explores the application of ensemble learning methods, such as bagging (e.g., Random Forest), boosting (e.g., AdaBoost, XGBoost), and stacking, in predicting heart disease. These methods leverage the strengths of diverse models to improve predictive accuracy, reduce overfitting, and enhance generalization. We discuss the challenges associated with heart disease prediction, including data imbalance, noise, and model interpretability, and how ensemble approaches address these issues. The paper also reviews performance metrics, model optimization techniques, and cross-validation strategies to evaluate ensemble models' effectiveness in heart disease classification. Finally, we examine future directions, including the integration of ensemble models into clinical decision support systems, advancements in model interpretability, and the potential for personalized healthcare. Ensemble learning represents a promising approach to improving heart disease prediction, providing clinicians with powerful, accurate tools to support early detection and intervention.
Heart disease remains one of the leading causes of death globally, making early and accurate prediction crucial for improving patient outcomes. Traditional methods of diagnosis, though effective, are increasingly supplemented by machine learning techniques, which can uncover complex patterns in large datasets. However, these models often face challenges such as overfitting, high false positive/negative rates, and difficulty in handling imbalanced datasets. Ensemble and hybrid machine learning methods offer promising solutions by combining multiple algorithms to enhance predictive accuracy, robustness, and generalization. Ensemble techniques, including bagging, boosting, and stacking, aggregate the strengths of various models to improve prediction performance, while hybrid models blend different learning paradigms (e.g., supervised and unsupervised) to leverage their complementary strengths. This paper explores the potential of ensemble and hybrid machine learning approaches for heart disease prediction, highlighting their advantages, challenges, and real-world applications. Through a comparative analysis, we demonstrate how these methods outperform traditional models in terms of accuracy and robustness, while also addressing the limitations of individual approaches. Finally, we discuss future directions, including the integration of these models into clinical decision support systems and their potential to contribute to personalized medicine.
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide, necessitating early detection and effective risk prediction to improve patient outcomes. Traditional methods of predicting CVD often rely on clinical assessments and risk scores, which, while valuable, have limitations in terms of accuracy and the ability to capture complex, nonlinear relationships between risk factors. Machine learning (ML), with its ability to analyze large, multidimensional datasets, has emerged as a promising approach for improving CVD prediction. This paper explores the application of various ML techniques, including supervised learning algorithms like decision trees, random forests, and support vector machines, as well as deep learning models, in the prediction of cardiovascular disease. We examine key data sources, such as the Framingham Heart Study and Cleveland Heart Disease dataset, and discuss challenges related to data quality, feature selection, and ethical concerns surrounding patient privacy. The paper also evaluates the performance of different ML models, highlighting metrics like accuracy, precision, recall, and interpretability, to assess their effectiveness in predicting CVD risk. Finally, we discuss emerging trends in CVD prediction, including the integration of wearable health devices, real-time monitoring, and explainable AI, which promise to further enhance the precision and applicability of ML models in clinical settings. The findings underscore the potential of machine learning to revolutionize cardiovascular risk assessment, offering personalized, data-driven insights that can ultimately reduce the global burden of cardiovascular disease.
Healthcare data is enabling physician in building predictive models and better patient profiles for more effective anticipation, diagnosis and treatment of various diseases. In addition, partnerships and collaborations between healthcare communities and researchers have resulted into the development of data pools, which can be used for establishing better personalized healthcare models. The increased use of artificial intelligence and machine learning is shifting the paradigm of medical research and treatment.These advanced technologies are providing researchers or medical practitioner real-timeaccess to every white paper and clinical case study conducted on a genetic disorder andalso help in detecting fraud in healthcare quickly and efficiently, with the use of statisticaltools and algorithms that can help a faster development of more accurately targetedvaccines with a cost effective ways for discovering more clinically relevant ways to analyses the disease and treat the patients in an effective manner. The objective of proposed approach to develop such mechanism to read patients clinical records, radiology data and provide the insight on the disease and allows researchers or medical practitioner to not only understand the full scope of a medical condition, but further shorten the amount of time it takes to develop a cure, treatment options, helping, and healing patients in need of healthcare.
Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction , with an accuracy level of 96%.
The data mining techniques-based systems could have a crucial impact on the employees’ lifestyle to predict heart diseases. There are many scientific papers, which use the techniques of data mining to predict heart diseases. However, limited scientific papers have addressed the four cross-validation techniques of splitting the data set that plays an important role in selecting the best technique for predicting heart disease. It is important to choose the optimal combination between the cross-validation techniques and the data mining, classification techniques that can enhance the performance of the prediction models. This paper aims to apply the four-cross-validation techniques (holdout, k-fold cross-validation, stratified k fold cross-validation, and repeated random) with the eight data mining, classification techniques (Linear Discriminant Analysis, Logistic regression, Support Vector Model, KNN, Decision Tree, Naïve Bayes, Random Forest, and Neural Network) to improve the accuracy of heart disease prediction and select the best prediction models. It analyzes these techniques on a small and large dataset collected from different data sources like Kaggle and the UCI machine-learning repository. The evaluation metrics like accuracy, precision, recall, and F-measure were used to measure the performance of prediction models. Experimentation is performed on two datasets, and the results show that when the dataset is colossal (70000 records), the optimal combination that achieves the highest accuracy is holdout cross-validation with the neural network with an accuracy of 71.82%. At the same time, Repeated Random with Random Forest considers the optimal combination in a small dataset (303 records) with an accuracy of 89.01%. The best models will be recommended to the physicians in business organizations to help them predicting heart disease in employees into one of two categories, cardiac and non-cardiac, at an early stage. The early detection of heart diseases in employees will improve productivity in the business organization.
Machine learning (ML) is also seen as an advanced technique that is only usable by highly qualified specialists. This prohibits this instrument from being utilized by many doctors and biologists in their studies. This paper’s purpose is to eradicate this obsolete perception. We claim that the recent creation of advanced high-performance ML techniques helps biomedical researchers to create competitive ML models rapidly without needing in-depth knowledge of the algorithms underlying them. This advanced system is implemented used best programming tool Python including two parts. Firstly, feature engineering and preprocessing with the Neighborhood Cleaning Rule (NCL) high-performance re-sampling procedure. Second, advanced models for high-performance machine learning, including AutoML, advanced XGBoost, and advanced ensemble bagging models. Finally, we believe that our developments would improve the way doctors interpret machine learning utilizing sophisticated and high-performance machine learning technologies and facilitate broad clinical use of Artificial Intelligence (AI) techniques.
Heart diseases also called Cardiovascular Diseases (CVD) include range of conditions portraying illness of heart. These include diseases related to blood vessels, rhythm problem, chest pain, heart attack, strokes, and fluctuating blood pressure. Person suffering with CVD has fluctuating blood flow rate. CVD are the leading cause of mortality in India including both male and female. A quarter of all mortality is attributed to cardiovascular diseases. Heart diseases and strokes are the pre-dominant causes and are responsible for > 80% of CVD deaths. Therefore in this paper a machine learning model is implemented on the dataset downloaded from kaggle. This dataset contains various parameters contributing to cardiac morbidity. It contains 70000 records and contains parameters like age, cholesterol, glucose, smoking, alcoholic habit etc. The decision Tree model is used fot training and predicting the risk of heart disease. The accuracy of implemented model is 73%.
Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.
New technologies such as machine learning and deep learning are being used in biomedical care, healthcare, and disease prediction. One major aspect is the early detection of diseases using machine learning. This paper will focus on the prediction of Coronary Heart Disease (CHD) using a risk factor approach. Learning techniques such as K-Nearest Neighbors, Binary Logistic Classification, and Naive Bayes will be used with a cross-comparative study. K-Fold's validation will be employed as well to create randomness in the data and to look at the consistency of the results produced by the models. Furthermore, hybrid models are also explored using ensemble techniques such as bagging, boosting, and stacking. These ensemble techniques are cross compared to the results of the original base classifiers. These algorithms are tested on the ‘Cardiovascular Disease Dataset,’ which has 70,000 records of medical examination of heart disease. Bagged models are shown to have an averaged increased accuracy of 1.96% in comparison to their traditional counterparts. Boosted models had an average accuracy of 73.4% but had the highest AUC score of 0.73. The stacked model involving KNN, random forest classifier, and SVM proved to be the most effective with an accuracy of 75.1%.
Determining the key features for the best model fitting in machine learning is not an easy task. The main objective of this study is to accurately predict cardiovascular disease by comparison among different feature selection algorithms. This study has employed a two-stage feature sub-set retrieving technique to achieve this goal: we first considered three well-established feature selection (filter, wrapper, embedded), and then, a feature sub-set was extracted using a Boolean process-based common “True” condition from these three algorithms. To justify the comparative accuracy and define the best predictive analytics, the well-known random forest, support vector classifier, k-nearest neighbors, Naive Bayes, and XGBoost model have been considered. The artificial neural network (ANN) has been considered as the benchmark for further comparison with all features. The experimental outcomes exhibit that the XGBoost Classifier integrated with the wrapper methods offers precise prediction results for cardiovascular disease. The proposed approach can also be applied in other domains such as sports analytics, bio-informatics, and financial analysis in contrast with healthcare informatics. This empirical study’s novelty is that the common “True” condition–based feature selection and comparison technique is entirely a new phenomenon in medical informatics.