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Pavithra V. is an Assistant Professor in the Department of Computer Applications at AJK College of Arts and Science, Coimbatore, Tamil Nadu, India. Her academic responsibilities include teaching and scholarly activities in computer applications. Her professional role supports the development of computing knowledge and skills. Her contribution focuses on AI-assisted kidney disease prediction and clinical decision support.
Renal illness represents a major worldwide health issue characterised by the gradual decline of kidney function, heightened cardiovascular risk, increased hospital admissions, and diminished quality of life. Chronic kidney disease (CKD) can progress slowly and often goes unnoticed in its initial phases, highlighting the necessity for prompt detection and suitable clinical oversight. This chapter explores the function of machine learning in forecasting renal illness, encompassing data collection, preprocessing, feature selection, model creation, assessment, interpretable artificial intelligence, and clinical decision assistance. A suggested framework is introduced for combining predictive modelling with personalised risk assessment, clinician-oriented explanations, and suitable follow-up procedures. The section additionally addresses data integrity, model adjustment, equitable algorithms, confidentiality, clinical verification, and implementation obstacles. Accurately verified machine learning algorithms can assist in identifying patients needing additional assessment, facilitate the monitoring of renal function, and enhance clinical decision-making. Predictive models should augment existing diagnostic standards and expert assessment instead of supplanting them.