Abstract
Heart disease prediction is a complex process that is influenced by several factors, including the combination of attributes leading to the possibility of heart disease and availability of these attributes in the database, an accurate selection of these attributes and determining the priority and impact of each of them on the prediction model, and finally selecting the appropriate classification technique to build the model. Most of the previous studies have used some heart disease symptoms as major risk factors to build a heart disease prediction system leading to inaccurate prediction results. The main objective of this study is to build an Adaptive Heart Disease Behavior-Based Prediction System (AHDBP) based on risk factors and behaviors that may lead to heart disease. Different classification algorithms will be deployed to get the most accurate results. 18 attributes were used to build the prediction system. The accuracy of the classification techniques was as follows: Decision Tree 90.34%, Naive Bayes 91.54%, and Neural Networks 94.91%. Neural networks can predict heart disease better than other techniques. The Chi square method has also been applied to determine the difference between the expected and the observed results, and the proposed system proved its accuracy at 86.54%.
Highlights
Heart disease is a serious disease that leads to death; the World Health Organization (WHO) announced that more than 12 million people die globally due to coronary diseases every year [1]
In order to enhance the accuracy of the system, we employed Adaptive Heart Disease Behavior-Based Prediction System (AHDBP) framework that run with different algorithms of the Decision Tree, Naive Bayes, and Neural Networks classification techniques
Data for more than 370 heart disease patients with 18 risk factors attributes were used for analysis, wrong attributes used in previous works are declared, identified and removed from the data
Summary
Heart disease is a serious disease that leads to death; the World Health Organization (WHO) announced that more than 12 million people die globally due to coronary diseases every year [1]. There are different types of ad categories of heart disease, such as coronary, cardiovascular and cardiomyopathic. The diagnosis of heart disease depends on a complex interaction between the patient‟s medical data and the doctor's experience in diagnosing the type of heart disease. This combination affects the quality of the offered medical care [3]. Misdiagnosis may harm the patient‟s health and is associated with financial and moral burdens. Medical patient data is rich with hidden information that not seen by the doctor, but it can used to improve heart diseases diagnosis [4]
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