Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases
Cancer; Cardiovascular Disease; Chronic Diseases; Chronic Kidney Disease; Diabetes; Early Detection; Machine Learning; Neural Networks; Prognostic Modeling; Random Forest
The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. 2025 IEEE.
Iqbal, J. L. Mazher; Gurrapu, Omprakash; Saritha, P.S.; Rajesh Kanna, R.; Jayanthi, R.; Tejaskumar, M.B.
Proceedings of 2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/COMP-SIF65618.2025.10969939 https://www.scopus.com/pages/publications/105004982694?origin=resultslist
Iqbal J.L.M., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Electronics and Communication Engineering, Avadi, India; Gurrapu O., Volvo Trucks, NC, United States; Saritha P.S., Dhanalakshmi Srinivasan College of Engineering, Department of Artificial intelligence and data science, Coimbatore, India; Rajesh Kanna R., Christ University, Department of Computer Science, Bangalore, India; Jayanthi R., Dayananda Sagar College of Engineering, Department of Master of Computer Applications, Bengaluru, India; Tejaskumar M.B., Don Bosco Institute of Technology, Bangalore, India