MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641101858 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And on August 24, 2026, for Predictive Analytics System And Method For Early Detection, Diagnosis, And Risk Stratification Of Chronic Kidney Disease.
Inventors include Mrs. Soma Sabitha; Mr. Salveru Vinay; Ms. Shanagarapu Nandini; and Ms. Chavatapalli Charishma Surya.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: In this invention, a Predictive Analytics Framework for Chronic Kidney Disease (CKD) Diagnosis and Risk Stratification is proposed to enable the early detection, classification, and progression assessment of Chronic Kidney Disease through the application of artificial intelligence, machine learning, and healthcare analytics techniques. The framework collects and integrates patient demographic information, clinical records, laboratory test results, electronic health records, and medical histories from multiple healthcare sources to create a comprehensive patient profile. The collected data undergoes preprocessing, normalization, and feature selection to identify significant clinical parameters associated with kidney disease, including serum creatinine, estimated glomerular filtration rate (eGFR), blood pressure, blood glucose levels, albumin concentration, hemoglobin levels, age, and other relevant health indicators. Advanced machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting are employed to analyze patient data, detect disease patterns, predict disease occurrence, and estimate progression risk. The framework further performs feature importance analysis to determine the most influential clinical factors affecting diagnosis and risk assessment, thereby improving transparency and interpretability of predictive outcomes. Based on the generated predictions, patients are classified into different risk categories ranging from low-risk to high-risk groups, enabling healthcare professionals to prioritize treatment plans and preventive interventions. The invention also incorporates a decision-support dashboard that provides real-time visualization of diagnostic predictions, risk scores, progression forecasts, and personalized clinical recommendations. Secure data handling mechanisms ensure confidentiality, integrity, and privacy of sensitive healthcare information throughout the analytical process. By integrating predictive analytics, intelligent risk stratification, machine learning-based diagnosis, and clinical decision-support capabilities within a unified framework, the invention significantly improves diagnostic accuracy, facilitates timely medical intervention, supports personalized patient management, reduces complications associated with late-stage kidney disease, and enhances overall healthcare outcomes across hospitals, clinics, diagnostic centers, and preventive healthcare systems.
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