Cardiovascular Disease Prediction Using ANN and Random Forest Algorithms

Machine Learning - Information Technology

Abstract: Heart-related diseases are on the rise, according to recent research by the World Health Organization. It kills 17.9 million people every year. As the population grows, early diagnosis and initiation of treatment becomes more difficult. But thanks to recent technological advancements, machine learning and deep learning techniques have accelerated the development of the health sector through several studies. With limited dataset and data features, a practical and composite data preprocessing, creative feature engineering methods examined in this project. Therefore, the purpose of this paper is to build a machine learning model for heart disease prediction based on relevant parameters. In this work, we used the Heart Disease Prediction benchmark dataset, which contains 14 different parameters related to heart disease. Machine learning algorithms such as Random Forest and ANN have been used for model development. We also try to find correlations between the different attributes available in the dataset with the help of standard machine learning methods, and then use them effectively to predict the chance of heart disease.

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