Intelligent Heart Disease Prediction System (IHDPS)

Jenny, Lim Yen Yen (2011) Intelligent Heart Disease Prediction System (IHDPS). Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

The objective of this project is to develop a web-based Naive Bayes decision support namely Intelligent Heart Disease Prediction System (IHDPS) to predict heart risk level of the user. This three-tier application is developed based on object-oriented analysis and design (OOAD) methodology. WAMP Server Version 2.0 is used to develop IHDPS. IHDPS is tested via white-box and black-box strategy. re five types of risk level can be provided for the heart disease prediction result which are Normal, Low, Medium, High and Very High. Besides, there are eight prediction parameters which can influence the heart disease risk level result re Age, Gender, Chest Pain Type, Resting Blood Pressure, Serum Cholesterol, Blood Sugar, Resting Electrocardiography Results, and Thallium Scan. re 303 data instances and 14 attributes exist in the system database to create a ge base for the Simple Naive Bayes classifier to carry out heart disease risk level prediction. The strengths of IHDPS are this system applies Simple Naive Bayes r for heart disease risk level prediction module, provides useful information about heart disease, can be accessed by users at anytime and also the user interfaces of this system are simple and user friendly. On the other hand, the weaknesses of are browser compatibility, lack of the system security feature, and also this has limited functionality and limited interaction with users. IHDPS can contribute on increasing awareness and vigilance about the heart disease to the users. re some future improvements for IHDPS which are solving browser compatibility issues, this system can add in e-mail or SMS notification and printing , and also can be upgraded to provide other kind of disease diagnosis such as to become a higher standard health care system.

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