MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641081791 A) filed by Sasireka. V; Dr Monika Gorkhe; Ms. Nikita Rajabhau Jaybhay; Chithirala Bala Subramanyam; Radhika Meegada; Ms. J. Pavalam; and Dr. Aarti Sangwan on July 02, 2026, for Ai And Machine Learning Based Predicting Potential Health Risk By Analyze Patient Data Using Strong Clustering Algorithms.
Inventors include Sasireka. V; Dr Monika Gorkhe; Ms. Nikita Rajabhau Jaybhay; Chithirala Bala Subramanyam; Radhika Meegada; Ms. J. Pavalam; and Dr. Aarti Sangwan.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: AI AND MACHINE LEARNING BASED PREDICTING POTENTIAL HEALTH RISK BY ANALYZE PATIENT DATA USING STRONG CLUSTERING ALGORITHMS ABSTRACT: Artificial intelligence (AI) is a transformative field of computer science capable of revolutionizing medical practice and healthcare delivery. Its applications encompass patient diagnostics, pharmaceutical research, enhanced communication, document transcription, and remote patient care. Artificial intelligence is transforming the care of chronic diseases, especially diabetes and cardiovascular conditions. The integration of information from AI-enabled models results in predicted accuracies over 80% about the beginning and course of sickness, facilitating early diagnosis, personalized therapy, and operational efficiency. Individuals with heart failure often experience unrecognized reductions in cardiorespiratory fitness, substantially elevating their risk of adverse consequences. Nonetheless, contemporary clinical practice is deficient in efficient instruments for the early classification of chronic renal failure risk. Decision-making in chronic diseases, informed by clinical decision support systems utilizing multifactorial models based on artificial intelligence, necessitates scientific validation across diverse populations to enhance the utilization of constrained human, financial, and clinical resources in global healthcare systems. An artificial intelligence-based clinical decision support system utilizing electronic medical data may assist in stratifying risk across populations with non-communicable diseases for enhanced decision making. Moreover, predictive analytics reduce emergency admissions and hospital readmissions by notifying physicians of high-risk patients before symptom aggravation occurs. Artificial Intelligence and Machine Learning present exceptional opportunities to revolutionize predictive healthcare through enhanced early intervention, customized treatment strategies, and the management of escalating healthcare expenses. AI predictive analytics is a technique that employs machine learning (ML), deep learning, and statistical modeling to identify trends and generate predictions from historical and real-time healthcare data. These systems utilize diverse inputs, such as electronic health records, laboratory results, wearable sensor data, genomic information, and socioeconomic variables, to identify subtle warning signs, predict clinical outcomes, and suggest optimal subsequent measures.
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