Enhancing Heart Disease Prediction: A Comparative Analysis of Machine Learning Models Using Extended Health Parameter Sets

The prospect of this study regards an improvement in the attention of machine learning models to some health hazard parameters, such as a heart disease prediction, in opposition to traditional complex modeling of cardiovascular health in machine learning. We work on the models that include standard clinical data as a part of traditional models together with new factors like regular lifestyle and real-time physiological information obtained from wearable devices. On an experimental side, comparison analysis of several machine learning algorithms was carried out, with the main focus on gradient boosting that is considered the most reliable in cases of complexity and large volumes of data. The results were impressive, and they clearly demonstrated a significant increase in prediction accuracy. The improved model obtained an accuracy rate of 95% with these new sources of revenue data incorporated compared to the initial 80% with traditional revenue sources solely used to model. This evidence confirms the necessity of including a multi-parametric equation in representing the evolution of health. The talk expounds upon these effects in the clinical setting, their existence perhaps being the opportunity for the doctors for the early approach to the patients and the adjustment of the treatment to every particular one. The study not only contributes to the field of disease diagnosis through the advanced methodology of predicting heart diseases but also stresses on the role of grouping health data from various sources. Future research will focus on the implementation of these models and additionally has longitudinal and genomic data challenges. These models will be extended to different communities.