Developing a Novel Cardiac Disease Prediction Framework Utilizing Advanced Machine Learning Algorithms

Abstract

Predicting and diagnosing cardiac disease has long been a crucial and difficult responsibility for medical professionals. Hospitals and other medical facilities provide pricey medicines and procedures to address cardiac ailments. Predicting cardiac disease in its early stages will thus be beneficial to the global population, allowing them to adopt preventative measures before the condition becomes serious. The study aims to revolutionize cardiac disease prediction and diagnosis through innovative machine learning methodologies. Addressing the challenge of early detection, which is crucial yet complex, the research seeks to implement a groundbreaking approach using advanced machine learning techniques. The novelty of this study lies in its use of two distinct machine learning algorithms - Logistic Regression and Random Forest - to analyze healthcare data. The obtained result shows that logistic regression model on the other hand had an accuracy of 80.48%, which is a fair performance, but still falls short of the random forest model's level of accuracy. This study will not only contribute to reducing mortality rates but also foster environments conducive to human development by enabling early intervention and effective treatment strategies. Data for this study is sourced from Kaggle, with Google Colab serving as the development platform, ensuring a robust and data-driven approach to cardiac healthcare.