Heart Disease Prediction using Case Based Reasoning (CBR)

TL;DR

This paper compares intelligent systems for heart disease prediction, finds CBR most accurate with 97.95%, and analyzes gender-based risk factors using a processed dataset.

Contribution

It introduces a comparative analysis of Fuzzy Logic, Neural Networks, and CBR, highlighting CBR's superior accuracy in heart disease prediction.

Findings

CBR achieved 97.95% accuracy in prediction.

Heart disease probability is 57.76% for males, 42.24% for females.

Smoking and alcohol are significant risk factors, especially for males.

Abstract

This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods relying solely on a doctor's experience often lack precision. To address this limitation, intelligent systems are applied as an alternative to traditional approaches. While various intelligent system methods exist, this study focuses on three: Fuzzy Logic, Neural Networks, and Case-Based Reasoning (CBR). A comparison of these techniques in terms of accuracy was conducted, and ultimately, Case-Based Reasoning (CBR) was selected for heart disease prediction. In the prediction phase, the heart disease dataset underwent data pre-processing to clean the data and data splitting to separate it into training and testing sets. The chosen intelligent system was then employed to predict heart disease outcomes based on the processed data. The experiment concluded with Case-Based Reasoning (CBR) achieving a notable accuracy rate of 97.95% in predicting heart disease. The findings also revealed that the probability of heart disease was 57.76% for males and 42.24% for females. Further analysis from related studies suggests that factors such as smoking and alcohol consumption are significant contributors to heart disease, particularly among males.