Artificial Intelligence in Healthcare: A Comprehensive Survey on Disease Prediction

Abstract: In recent years, machine learning has found growing application in clinical settings, particularly for automating disease-level risk assessment tasks that were historically reliant on physician judgment alone. This paper surveys the current state of AI-driven healthcare systems, drawing on peer-reviewed publications indexed in IEEE Xplore, Springer, ScienceDirect, and PubMed. We reviewed work spanning core algorithmic approaches—including Random Forest, SVM, KNN, and various neural architectures—alongside applied systems for symptom triage, drug safety screening, and patient-facing health assistants. To bring structure to this diverse body of literature, we introduce a four-tier classification scheme organized around increasing system sophistication: from basic symptom-to-diagnosis mapping, through individualized care recommendations, into clinical support tooling, and finally toward fully integrated AI health platforms. Performance dimensions examined include classification accuracy, precision-recall balance, response latency, and scalability characteristics. A recurring pattern across reviewed studies is the absence of any single system that simultaneously covers real-time symptom intake, medication interaction checking, personalized guidance, and conversational AI interaction within one coherent architecture. We discuss the practical and theoretical implications of this gap and sketch directions for future work.

Keywords: Artificial Intelligence; Healthcare Systems; Disease Prediction; Machine Learning; Random Forest; Support Vector Machine; K-Nearest Neighbors; Neural Networks; Symptom Analysis; Drug Interaction; Medication Safety; Clinical Decision Support; Personalized Medicine; Healthcare Chatbot; Predictive Analytics.