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K. Sandhya Rani is a Lecturer in Computer Science at Government Degree College, Dharpally, Nizamabad District, Telangana, India. Her professional responsibilities include teaching and supporting students in computer science. Her academic role contributes to computing education at the degree level. Her contribution to this chapter concerns machine learning approaches to kidney disease prediction.
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.