"Machine Learning Models for Healthcare Decision Support Systems" highlights key developments made possible by enhanced models displaying higher levels of accuracy and sensitivity. Extensive research and experiments were conducted to develop new ML models specifically for healthcare applications. These models outperform prior ones in terms of sensitivity and accuracy. In addition to improved performance, these models include an emphasis on interpretability, which is vital for healthcare decision-making because it provides transparent insights. Improved patient outcomes may result from more accurate diagnoses, tailored therapy suggestions, and other benefits offered by these state-of-the-art ML models, according to the results. By laying the groundwork for more efficient and trustworthy decision support in clinical practice, this study greatly adds to the continuing endeavors to advance healthcare by employing ML technologies.

medical recommendation system; RF algorithm; Machie Learning; deep learning