Abstract
This paper aims to design and implement an automatic heart disease diagnosis system using MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing, where 80% and 20% of the Cleveland data set were randomly selected for training and testing purposes respectively. Each system also has an additional module known as case-based module, where the user has to input values for 13 required attributes as specified by the Cleveland data set, in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.
Highlights
Heart disease has become one of the most prevalent diseases which people are being suffered from
This paper presents a decision support system for heart disease classification using neural network
It is important to highlight that there are two main tests conducted, the first at the training module, where the training data set is tested against the trained Neural Network and Neuro-Fuzzy, and the second at the testing module, where the testing data set is tested against the trained Neural Network and Neuro-Fuzzy
Summary
Heart disease has become one of the most prevalent diseases which people are being suffered from According to statistics, it is one of the most important causes of deaths all over the world (CDC’s report). Diagnosing of the heart disease is an essential matter in health care industry and many researchers try to develop medical decision support systems (MDSS) to help physicians These systems are developed to moderate the diagnosis time and enhance the diagnosis accuracy in addition to supporting increasingly complicated diagnosis decision process [1] [2]. Hospital information systems using decision support systems have different tools available to obtain data, but they are still restricted These tools can just answer some simple queries like “identifying the male patients who are below 20 years old, and single who have been treated for heart attack”. They are not able to answer complex queries “given patient records, predicting the probability of patients getting a heart disease” as an example [3]
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