MedAssist : Health Analyze Prescription Suggestion System

Abstract

This paper presents a holistic exploration of technological advancements in healthcare, focusing on two innovative approaches: disease prediction systems driven by Ma-chine Learning (ML) algorithms and medication suggestion applications aimed at optimizing medication management. Synthesizing insights from diverse research endeavors, we propose a unified framework named "SmartCare" for disease prediction, leveraging ML algorithms such as Na¨ıve Bayes, Decision Tree, and K-Nearest Neighbors (KNN) to enhance accuracy and efficiency in diagnosis. Concurrently, we delve into the development and evaluation of medication suggestion applications tailored to individual patient profiles, utilizing patient-specific data and evidence-based guidelines to improve medication adherence and clinical outcomes. Through rigorous randomized controlled trials, we demonstrate the efficacy of these applications, highlighting significant improvements in medication adherence, clinical parameters, and patient satisfaction. By integrating these technological advancements into healthcare, we aim to revolutionize patient care, enhance treatment outcomes, and foster a more patient-centric healthcare ecosystem, while emphasizing the need for further research to refine and optimize these approaches for broader implementation across diverse patient populations and healthcare settings.