Machine Learning-Based Early Prediction and Risk Assessment of Chronic Diseases

Shaik Muthahar, V.Madhumitha, Shashank Shekhar Tiwari. In: Next-Generation AI-Driven Healthcare Systems for Disease Prediction, Diagnosis and Therapeutic Optimization — ISBN: 978-81-17543-07-8. Pages: 1 - 22

Chronic illnesses, such as cardiovascular ailments, diabetes, chronic renal disease, persistent respiratory conditions, and certain malignancies, pose significant obstacles to worldwide healthcare frameworks. ML has surfaced as a viable method for discerning intricate connections among demographic attributes, clinical assessments, lifestyle variables, medical backgrounds. The section additionally explores assessment criteria, validation methodologies, equity, privacy concerns, cybersecurity, model interpretability, and obstacles to clinical deployment. Special emphasis is placed on differentiating statistical prediction, clinical diagnosis, and personalised risk assessment. Future investigative avenues encompass multimodal health analytics, personalised prevention, and the ethical use of AI into healthcare processes. The chapter establishes a theoretical basis for creating dependable, comprehensible, and clinically pertinent systems for the early forecasting and risk evaluation of chronic illnesses.