Artificial Intelligence and Machine Learning for Intelligent Healthcare Diagnosis: A Comprehensive Survey

Artificial Intelligence and Machine learning (ML) have emerged as a powerful technology for improving intelligent healthcare diagnosis and clinical decision support systems. The rapid growth of healthcare data, including electronic health records, medical imaging, and physiological signals, has created new opportunities for applying advanced computational techniques to disease prediction and early diagnosis. Machine learning models such as Support Vector Machines, Random Forest, Artificial Neural Networks, and Convolutional Neural Networks have demonstrated significant potential in detecting various diseases including cancer, cardiovascular disorders, neurological diseases, and respiratory conditions. These models can analyze complex medical datasets and identify hidden patterns that support accurate and timely medical decisions. This paper presents a comprehensive survey of machine learning approaches used in intelligent healthcare diagnosis. The study reviews commonly used algorithms, datasets, and evaluation metrics while highlighting key application areas in medical diagnosis. Furthermore, the survey discusses major research challenges such as data imbalance, model interpretability, and privacy concerns, and outlines future research directions for developing reliable and scalable ML-based healthcare systems.