Authors : J.C. Bambal; Supriya P. Umale; Ravindra M. Andhale; Janhavi H. Kale; Roshan M. Gajabhe; Dnyanesh P. Sontakke

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/y7pevbcm

Scribd : https://tinyurl.com/4en34bvu

DOI : https://doi.org/10.38124/ijisrt/26mar646

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Smart healthcare systems powered by Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern healthcare services by enabling early disease prediction and personalized medical assistance. The Smart Predictive HealthCore system aims to analyze user symptoms using machine learning algorithms and predict possible diseases while providing health recommendations. This review paper analyzes existing research on AI-based healthcare prediction systems, machine learning techniques used in disease prediction, and the integration of intelligent technologies such as chatbots and location services for healthcare accessibility. The study reviews literature from recent years to understand current technological trends, system architectures, implementation methodologies, and limitations in predictive healthcare systems. The findings show that combining machine learning models such as Random Forest, Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN) with web technologies can significantly improve early disease detection and healthcare accessibility.

Keywords : Machine Learning, Disease Prediction, AI Chatbot, Healthcare System, Random Forest, SVC, Google Maps API.

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