Advancing Healthcare Predictions: Harnessing Machine Learning for Accurate Health Index Prognosis

Abstract

This study investigates how machine learning (ML) techniques may be used to forecast health indicators' accuracy, which is important for efficient medical monitoring and diagnosis. Numerous machine learning techniques, such as Support Vector Machines and Random Forest, are evaluated by using a heterogeneous dataset that includes vital signs, lab findings, and patient information. Model performance is optimised by careful preprocessing and feature engineering, which includes managing missing variables and normalisation. Model accuracy is further improved via hyperparameter tuning strategies, which are measured using metrics like precision and recall. The findings show that machine learning (ML) models can accurately predict health index accuracy, which may help with early illness identification and individualised treatment plans. The study highlights the potential of machine learning in healthcare decision-making and provides guidance for raising the standard of patient care. Future projects could look into adding more functionality and integrating real-time data for.