Advancement in Healthcare Systems by Automated Disease Diagnostic Process Using Machine Learning

E-adoption of emerging technology plays an important role during the pandemic. The COVID-19 pandemic taught us that everyone must make himself healthy and immune to viral disease. Diabetes is the most common disease in the Indian population found in people of every age. The objective of this research work is to use the emerging technologies such as machine learning to implement e-adoption in the healthcare system. The proposed methodology can predict the diabetes disease by using vital parameters like age, glucose level, blood pressure, etc. This proposed model is implemented into Python programming language and various machine learning classifiers such as random forest, decision tree, logistic regression, and XGBoost are used on PIMA database. Thereafter, comparative analysis is performed to test which technique is better for predicting and diagnosing diabetes disease. The method founds XGBoost classifier gives the highest accuracy (i.e., 84%) among all classifiers with single database and single classifier.