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Objective: The aim of this study was to optimize the performance of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of its connection weights which is usually computed based on trial and error when used to diagnose Typhoid fever patients.
Methods: This research proposed the use of Genetic Algorithm (GA) technique to automatically evolve o...
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... dataset was partitioned into three parts, the first (70%) was used for training the network, the second (15%) was used to validate the trained network, while the remaining part (15%) was used to test the performance of the proposed system. Figure 7 shows the Genetic Algorithm module built to automatically compute the connection weights of the Neural Network component of the proposed system. This module computes the best set of weights needed by the hidden layers of the NN for training. ...
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... Elitism in the context of evolution involves the possibility of individuals suffering damage that leads to a reduction in their quality. Elitism, however, serves as a mechanism that shields the top-performing individuals from undergoing further evolutionary changes [46], [47]. Instead, these exceptional individuals are directly passed on to the next generation without any alterations. ...
Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
... In a similar study from 2014, the results of surgery are compared with optimized medical therapy over a period of 3 years. The results of the operation were dramatically superior, with near normalization of glycated hemoglobin levels and virtually no need for anti-diabetic medication (Asogbon et al. 2016). ...
Diabetes is one of the major diseases prevalent today affecting around 400 million people worldwide. Approximately one in ten adult people worldwide have diabetes. Unfortunately, about half of them live in rural areas and are not aware about the severity of the disease. Treatment of diabetes is feasible, but also challenging and expensive. Our contribution is to develop a reference model in assisting rural people suffering from diabetes. It helps the rural people of India in characterizing the victims of diabetes 2 at the earlier stages. This model improves the communication and interaction between patients and doctors. The target of analysis made in the present research is to list the risks factors and correlation that exist among those risk factors. In this work, logistic regression, support vector machine, random forest, decision tree, Naive Bayes, K nearest neighbor classifiers are used for prediction, and their accuracy is compared to choose the better machine learning model. SVM provides higher accuracy (96.0) among the choosen algorithms.
... Next year, genetic NFS approach (GANFIS) was applied for diagnosis of typhoid fever by Asogbon et al. (2016). The experiments were conducted on the data set obtained from Federal medical center, Nigeria; containing 104 typhoid instances each with 4 parameters regarding laboratory test such as liver function test, blood test and so on; 8 parameters regarding medical history of the patient such as fever, headache, abdominal pain and so on, and physical examination parameters such as body temperature and pulse rate. ...
In classification and prediction of different types of medical disorders the neuro-fuzzy systems (NFS) are playing vital and significant role. To avoid false diagnosis the NFS assists medical practitioners to a greater extent in automating the domain dealing with medical disorders. With the passage of time the NFS approach has become apparent to enhance accuracy in dealing with a wide range of complicated research problems in the field of medical diagnosis. In this paper the author presents the literature review of the research done in implementing NFS in the field of medical diagnosis for current decade. Total of 100 publications in chronological advancement and up-gradation in models are considered for the time period of 10 years. A detailed study of each disease is carried out to discuss how NFS methodologies have been applied for classification and prediction in the diagnosis of different types of medical disorders. Ten (10) most severe medical disorders i.e. cancer, cardiovascular, depression and anxiety, diabetes, communicable, kidney, liver, neuro-degenerative, respiratory and thyroid has been undertaken for the study. Based on the study carried out it has been observed that NFS found to be effective as compared to the application of other AI techniques in medical diagnosis. Study reveals that effectiveness of NFS increases significantly when integrated with other AI approaches. This review adds into the knowledge of different researchers working in the field of medical diagnosis and will also give the comprehensive view of the effectiveness of the NFS techniques being used in medical diagnosis. The paper also incorporates a few research publications that were submitted in 2019 to incorporate the latest advances in medical science implementation of NFS.
... The number of particles was set to 125, and maximum number of iterations was set to 1000. The inertia weight and maximum velocity value were set to 0.9 and 2. In the GA setting, the single point crossover rate and mutation rate were set to 0.8 and 0.01 [4,19]. ...
In recent years, artificial intelligence and bio-inspired computing methodologies have risen rapidly and have been successfully applied to many fields. Bio-inspired network systems are a field of biology and computer science, it has the high relation to the bio-inspired computing and bio-inspired system. It has the self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease in the network environment. Software-defined networking provides a breakthrough in network transformation. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. More recently, the artificial bee colony (ABC) algorithm has been used to solve the issues of parameter optimization. In this paper, a discretized food source for an artificial bee colony (DfABC) optimization algorithm is proposed and applied to optimize the kernel parameters of a support vector machine (SVM) model, creating a new hybrid. In order to further improve prediction accuracy, the proposed DfABC algorithm is applied to six popular UCI datasets. We also compare the DfABC algorithm to particle swarm optimization (PSO), the genetic algorithm (GA), and the original ABC algorithm. The experimental results show that the proposed DfABC-SVM model achieves better classification accuracy with a shorter convergence time, outperforming the other hybrid artificial intelligence models.
... In addition, there are other studies conducted with a different platform. [10] and [11] developed systems that diagnose Typhoid Fever. These are an advancement on the performance of typhoid fever diagnosis systems based on neuro-fuzzy, but its applicability on other fever types are not considered, as well as symptoms interrelationship among different fever types are not also considered in the neuro-fuzzy system. ...
... Both studies are also tried and tested with Matlab software hence, these are not available for a wide range of users. [11] system uses a genetic algorithm to enhance the optimum performance of neuro-fuzzy systems but it has the accuracy rate of 92.73% giving an error rate of 7.27%. In 2012, a system [12] which diagnoses the risk in dengue patients is developed. ...
Fever is a sign that the body is trying to fight
infection. It is usually accompanied by various sicknesses
or symptoms that signal another illness or disease.
Diagnosing it ahead of time is essential because it has to
do with human life and to determine what to do to get
well. MeDevice is a mobile-based application that runs in
Android devices that allows the user to enter the levels of
his/her symptoms and diagnoses the disease either as
influenza, dengue, chicken pox, malaria, typhoid fever,
measles, Hepatitis A and pneumonia together with its
details and its first aid treatment. It aims at providing an
efficient decision support platform to aid people with
fever in diagnosing their disease and whether or not to
seek medical attention especially in developing countries
like the Philippines. This application is engineered with
the knowledge base and the inference method of fuzzy
logic and expert system with the help of Gradient Descent
optimization algorithm and back propagation neural
network to achieve the optimum value of the error rate.
This is essential to provide the application with a high
accuracy rate which shows during the conduct of testing
of the application.
... For future work, we suggest applying the proposed algorithm to other cloud problems such as multi-objective VM placement, dynamic migration, and the cloud tasks scheduling problem. Also, it can be applied to other emerging applications such as Medical Diagnosis [66,67], healthcare delivery organizations [68]. Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. ...
The consolidation of virtual machine (VM) is the strategy of efficient and intelligent use of cloud datacenters resources. One of the important subproblems of VM consolidation is VM placement problem. The main objective of VM placement problem is to minimize the number of running physical machines or hosts in cloud datacenters. This paper focuses on solving VM placement problem with respect to the available bandwidth which is formulated as variable sized bin packing problem. Moreover, a new bandwidth allocation policy is developed and hybridized with an improved variant of whale optimization algorithm (WOA) called improved Lévy based whale optimization algorithm. Cloudsim toolkit is used in order to test the validity of the proposed algorithm on 25 different data sets that generated randomly and compared with many optimization algorithms including: WOA, first fit, best fit, particle swarm optimization, genetic algorithm, and intelligent tuned harmony search. The obtained results are analyzed by Friedman test which indicates the prosperity of the proposed algorithm for minimizing the number of running physical machine.
... Based on the problem of the number of typhoid fever sufferers and requires accuracy in diagnosing typhoid fever based on indication caused, it is necessary to use the Neuro Fuzzy method which will then be optimized again using Genetic Algorithms and implemented into the system. The proposed system is built with the aim of providing a decision support which adopts genetic algorithm technique to optimize the performance of a neuro-fuzzy diagnostic method platform with the capability to aid medical practitioners in administering efficient diagnosis outcome for patients [16]. So that it is expected to help speed up the diagnosis of typhoid fever that may be suffered. ...
Neuro Fuzzy is one method in the field of information technology used in diagnosing an disease. The application of Neuro Fuzzy is to identify disease. Genetic algorithms can be used to find solutions without paying attention to the subject matter specifically, one of which is an optimization problem. Typhoid or typhoid fever is a disease caused by Salmonella enterica bacteria, especially its derivatives. The diagnosis of typhoid fever is not an easy thing to do. This is because some of the indications experienced by patients also appear in other diseases. The number of patients with typhoid fever that requires accuracy in diagnosing typhoid fever based on indications caused. Based on this background this study aims to assist in the diagnosis of typhoid fever with 11 indication variables. This study uses medical record data for typhoid fever in 2017 Tidar Magelang Hospital. The method used is Neuro Fuzzy which optimizes the value of the degree of membership with genetic Algorithms. Then the value of the degree of neuro fuzzy membership is more optimal. The results of this optimization are the diagnosis of typhoid fever based on the variable of indications entered. From the research results obtained from the neuro fuzzy method get an 80% accuracy value and neuro fuzzy optimization results with genetic algorithms with a value of pc 0.5, pm 0.2 and max generation 25 the value of accuracy increases to 90%. Suggestions from this study, need to add more specific indication variables.
... The technique is able to deal with problems that involve uncertain, imprecise and ambiguous data. Among previous works on fuzzy logic based CAD system include [7] for Thalassemia disease, [8] (2017) for liver disease and [9] for thypoid fever. For heart disease diagnosis, there are various fuzzy logic based CAD systems that have been developed [3,[10][11][12][13][14]. ...
The diagnosis of coronary heart disease is a non-trivial task that requires a careful and time-consuming examination. Hence, the application of a computer-aided diagnostic (CAD) system to assess the condition of a person of having coronary heart disease is greatly beneficial. Although the usage of CADs related to coronary heart disease diagnosis is widely implemented, the inputs medical data required is still a significant challenge to encourage rapid diagnosis among public. This paper presents a fuzzy logic system for diagnosing coronary heart disease. In this study, the fuzzy logic based CAD system has been developed with five input variables that requires minimal medical procedure to obtain to determine the presence of coronary heart disease. Experiments to assess the accuracy of the system and comparison with a previous work of rough-fuzzy classifier were carried out using an open source coronary heart disease dataset. The results show that the proposed work is able to achieve significant accuracy of 72.6%, an improvement of 30% from the previous work.
... Their result shows significant success and adaption of artificial intelligence technique in expert computer aided medical diagnosis systems. (3) Furthermore, to enhance the accuracy of artificial intelligent based expert medical diagnosis system, [28] developed a genetic algorithm to enhance optimum performance of neuro-fuzzy systems used for diagnosis of typhoid. This is to ensure accuracy of result, which in an ordinary neuron fuzzy based typhoid fever diagnosis system, is computed with errors. ...
... Artificial neural network (ANN), a family of statistical learning algorithms inspired by biological neural networks, it has been applied to the modeling of complex decision making problems across different domains [20]. Such a network consists of highly inter-connected processing elements that demonstrate the ability to learn and generalize from training patterns or data. ...
... Performance evaluation is an important part of any developed system especially those tailored towards medical diagnosis where valid and high prediction rate are of significance. Such evaluation often shows how reliable a developed system/method is in providing efficient solution [20]. To evaluate a CI based prediction system such as the ones described above, several evaluation metrics have been proposed among which prediction (classification) accuracy, sensitivity (Recall), specificity, precision, F1-Measure, false positive rate, and false negative rate have been widely adopted primarily because they better reflect the true performance of the system. ...
