ANALYSIS OF HEART FAILURE PREDICTION WITH RANDOM FOREST ALGORITHM AND LINEAR REGRESSION

Ismar Hidayat; Muhammad Iqbal; Leni Marlina; Andysah Putera Utama Siahaan; Zulham Sitorus

Authors

Ismar Hidayat Universitas Pembangunan Panca Budi
Muhammad Iqbal Universitas Pembangunan Panca Budi
Leni Marlina Universitas Pembangunan Panca Budi
Andysah Putera Utama Siahaan Universitas Pembangunan Panca Budi
Zulham Sitorus Universitas Pembangunan Panca Budi

Keywords:

Predictions, Heart Failure, Random Forest, Linear Regression, Pembelajaran mesin

Abstract

Predicting the risk of heart failure is an important step in the prevention and early treatment of potentially fatal cardiovascular diseases. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Linear Regression, in predicting heart failure based on patient data that includes variables such as age, blood pressure, cholesterol levels, and other health history. The results show that the Random Forest algorithm is significantly superior in terms of prediction accuracy compared to Linear Regression, especially on data with a pattern of the number of data used. However, Linear Regression remains relevant in providing more stable results on differences in the amount of data used and has a more significant effect on the variables of heart failure. Therefore, a Random Forest-based prediction model is recommended to predict heart failure if it has a large amount of tranning data, and Linear Regression is recommended for prediction stability. The implementation of this model is expected to help medical practitioners in making more appropriate and accurate decisions to prevent the occurrence of heart failure in high-risk patients.

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