Page No: 30-38-

Volume issue & Publishing Year: Volume 2 Issue 4,April-2025

Journal: International Journal of Modern Engineering and Management | IJMEM

ISSN NO: 3048-8230

DOI:

Abstract:

The use of machine learning (ML) and artificial intelligence (AI) have sped up the development of solutions in healthcare. These solutions can move from static rule-based systems to adaptive, data-driven solutions and allow for faster, more intelligent diagnoses, prognostics, and treatment plans.  In this study, we provide a more sophisticated, complete end-to-end medical recommendation system and the potential to not only predict diseases based on user-reported symptoms, but also recommend tailored advice on medication prescriptions, diets, exercise plans, and other relevant actions. Essentially, the system uses an assortment of supervised machine learning algorithms to optimize disease diagnosis using Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM) models. However, our software takes the process further since we provide actionable recommendations in addition to predictions. In order for patients to access effective treatment options when the first-line medications are not suitable or available, we employ cosine similarity algorithms to pair any medication with alternatives. Along with prescribing medications, the system offers individualized diets and exercise programs based on each projected disease. This holistic approach considers both short-term treatment needs and long-term health goals to help people overcome nutritional deficits and chronic symptoms and establish healthy habits that last. Our frame of reference is shifting to a holistic problem-solving model as compared to earlier predictive models that simply worked to identify sickness. The capabilities are available through a scalable and easy-to-use web interface built in Flask. The seamless transition between managing medications, lifestyle coaching, and predicting disease suggests that AI-powered platforms can formulate from lab prototypes into functional tools to manage health on a daily basis.

Keywords:

Disease Prediction, Flask-Based Healthcare Platform, Machine Learning, Symptom-Based Diagnosis

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