Heart Disease Prediction Using Decision Tree Analysis

Princess Clair C. Clarite; Inna Vita Grace Vanya G. Palma

doi:10.69693/jesa.v1i2.9

Authors

Princess Clair C. Clarite Department of Computer Science, North Eastern Mindanao State University
Inna Vita Grace Vanya G. Palma Department of Computer Science, North Eastern Mindanao State University

DOI:

https://doi.org/10.69693/jesa.v1i2.9

Keywords:

Heart Disease, Matlab, Decision Tree Analysis, Prediction

Abstract

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. The objective of the research is to use significant features and factors to design a prediction algorithm using Machine learning. The goal is to accurately classify whether a person has heart disease or not. The dataset contains 1025 records. The records are divided into two categories positive those who have heart disease and negative those who do not. It is currently standard practice to divide the data at random into roughly 70% for training and 30% for testing. The researcher used the MATLAB software tool to create a decision tree model of heart disease prediction. Using a decision tree model, it was determined that the best indicator that someone has heart disease is chest pain.

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