• Mahesh Kakasaheb Hasabe
• Kishor Mane

The pivotal role of data processing in the Knowledge Discovery in Databases (KDD), employing techniques such as classification, clustering, and association to extract knowledge from large datasets. The field of artificial intelligence has seen significant advancements in machine learning techniques. These developments show promise in supporting healthcare professionals, particularly in enhancing the accuracy of medical diagnoses. The synergy between machine learning and data mining is underscored for enhancing system efficiency. It also delves into the characteristics of datasets in machine learning algorithms, distinguishing between continuous, categorical, and binary features, and the distinction between supervised and unsupervised learning. This study investigates the application of computational methods, specifically data mining and machine learning algorithms, to forecast the occurrence of long-term health conditions. With a particular emphasis on one specific condition. The study highlights the significance of early detection for individuals who may be at higher risk due to several health factors. These risk factors include high blood pressure, heart-related issues, elevated glucose levels, or a genetic predisposition to the disease. By applying advanced analytical methods, the aim is to improve the accuracy and timeliness of identifying potential cases, thereby enabling more effective preventive measures and interventions. The paper underlines the differentiation between Chronic Disease and acute kidney disease, stressing the need for persistent reduction in kidney function over three months for the former. Overall, the paper highlights the critical role of predictive analytics in addressing chronic kidney disease as a significant healthcare concern along with different techniques used by researchers.

Deep learning, Machine Learning, Chronic Disease detection, Electronic Medical Records (EMR)

"Different Techniques of Chronic Disease Detection using Machine Learning: Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.f86-f91, July-2024, Available :http://www.jetir.org/papers/JETIR2407513.pdf

"Different Techniques of Chronic Disease Detection using Machine Learning: Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppf86-f91, July-2024, Available at : http://www.jetir.org/papers/JETIR2407513.pdf