Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction

Abstract

Heart disease prediction is a critical area in healthcare, as early identification and accurate assessment of cardiovascular risks can lead to improved patient outcomes. This study explores the application of machine learning techniques for predicting heart disease. Various data attributes, including medical history, clinical measurements, and lifestyle factors, are utilized to develop predictive models. A comprehensive analysis of different machine learning algorithms is conducted to determine their efficacy in classification tasks. The dataset used for experimentation is sourced from a diverse patient population, enhancing the generalizability of the findings. Through rigorous evaluation and validation, the study aims to identify the most suitable machine learning approach for effectively predicting heart disease. The results highlight the potential of machine learning as a valuable tool in assisting healthcare professionals in making informed decisions and providing personalized care to individuals at risk of heart disease