Genomics at the Nexus of AI, Computer Vision, and Machine Learning

3Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors

Shilpa Choudhary^1*, Monali Gulhane², Sandeep Kumar³, Nitin Rakesh², Sudhanshu Maurya² and Chanderdeep Tandon⁴

¹Department of CSE (AIML), Neil Gogte Institute of Technology, Hyderabad, India

²Department of CSE, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

³Department of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, India

⁴Department of CSE, Amity School of Biological Sciences, Amity University Punjab, Mohali, Punjab, India

Abstract

Kidney stones are a substantial health risk, and effectively preventing them necessitates precise and individualized evaluations of risk. This work presents a clinical decision assistance system that compares logistic regression, assistance vector machine (SVM) and random forest models. These models employ an extensive range of characteristics, such as CLDN11 genetic information, familial background, dietary habits, and fluid consumption, to offer a complete method for evaluating the risk of kidney stone formation. The models were initially trained using the GesNet dataset, a rich repository of clinical and genetic information about kidney stones. Prospective research was done to assess the system’s performance in an actual clinical environment. A fresh group of participants was enlisted, and CLDN11 genetic data and comprehensive ...