Computational Analysis of Diabetes Kidney Diseases Using Machine Learning

The increasing complexity of healthcare, coupled with an ageing population, poses significant challenges for decision-making in healthcare delivery. Implementing smart decision support systems can indeed alleviate some of these challenges by providing clinicians with timely and personalized insights. These systems can leverage vast amounts of patient data, advanced analytics, and predictive modeling to offer clinicians a comprehensive view of individual patient needs and potential outcomes. Currently, researchers and doctors need a faster solution for various diseases in health care. So they started to use the Machine Learning (ML) algorithms for better solution. ML is a sub field of Artificial Intelligence (AI) that provides a useful tool for data analysis, automatic process and others for healthcare system. The use of ML is increasing continuously in healthcare system due to its learning power. In this paper the following algorithms are used for the diagnosis of Diabetes and Kidney Disease such as: Gradient Boosting Classifier (GBC), Random Forest Classifier (RFC), Extra Trees Classifier (ETC), Support Vector Classifier (SVC) and Multilayer Perceptron (MNP) Neural Network, In our model, Gradient Boosting Classifier is used with repeated cross validation to develop our system for better results. The experiment analysis performed for both unbalanced and balanced dataset. The accuracy achieved in case of unbalanced and balanced datasets for GBC, ETC, RFC SVC, MLP & DTC are 75.7 & 92.2, 75.7 & 90.1, 74.4 & 80.0, 62.5 & 66.4, 58.3 & 63.0 and 59.4 & 74.5 respectively. On comparing these results, we found that GBC results are better than other algorithms.