AI-Powered Heart Health: Predictive Analytics for Early Disease Detection

Abstract

Heart infection is a driving cause of mortality around the world, making early conclusion significant for successful administration. With progressions in machine learning (ML), prescient analytics has ended up a profitable instrument for recognizing high-risk patients and fitting personalized treatment procedures. This study applies various ML algorithms to clinical data for predicting and classifying heart disease. Techniques such as data preprocessing, feature selection, and model optimization are employed to enhance prediction accuracy. the calculations assessed incorporate Calculated Relapse, Choice Trees, Irregular Timberland, Back Vector Machines (SVM), and Neural Systems. Their execution is evaluated based on exactness, exactness, review, F1-score, and ROC-AUC. The findings demonstrate that ML models can significantly improve early diagnosis, enhancing patient outcomes. By identifying the best-suited algorithms for heart disease prediction, this research provides insights into integrating ML in clinical settings for more accurate and timely intervention. The study concludes with a discussion on the implications for healthcare providers and future directions in predictive healthcare analytics, highlighting the potential of ML to revolutionize heart disease management.

Ähnliche Arbeiten