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Dr. B. Chandra Sekhar is an Assistant Professor in the Department of Mathematics at Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India. His academic responsibilities include teaching and research in mathematics. His professional role contributes to mathematical education in engineering. His contribution to this chapter concerns computational methods for disease progression and treatment-response prediction.
Machine learning (ML) is increasingly being investigated for predicting disease progression and treatment response using clinical records, laboratory measurements, medical imaging, physiological signals, genomic information, and longitudinal patient observations. Conventional healthcare assessment often relies on periodic clinical evaluations and population-level treatment guidelines, which may not fully capture individual differences in disease trajectories or therapeutic outcomes. It discusses supervised learning, ensemble algorithms, deep learning, survival analysis, time-series modelling, and causal inference approaches for different clinical prediction tasks. A conceptual framework is presented that integrates data acquisition, preprocessing, feature engineering, model development, longitudinal prediction, interpretability, and clinical validation. The chapter also explores applications in oncology, cardiovascular disease, diabetes, neurological disorders, and chronic inflammatory conditions.