Abstract:
Millions of individuals worldwide suffer with diabetes mellitus, a chronic metabolic ailment, and early diagnosis is essential to controlling the condition and avoiding complications. The development of precise prediction systems for a variety of medical illnesses has shown encouraging outcomes in recent years thanks to machine learning techniques. The goal of this study is to employ machine learning to create a diabetic prediction system that is specifically created for females. The planned study will examine a dataset that includes detailed health records for females, together with data on their lifestyle choices, medical histories, and demographics. The dataset will be processed using a suitable machine learning technique to produce a predictive model. The accuracy, recall, f-measure and precision of various algorithms will be compared in order to determine which is the most efficient. I'm using the Pima Indian Diabetes Database data set from Kaggle.com for this research. In this work I have used some machine learning algorithms which is logistic regression, Naive-bayes, support vector machine (SVM), decision tree and Random Forest. After preprocessing the data set and applying this algorithms the prediction performance of diabetics disease analysis shows that random forest obtained the uppermost performance with the accuracy of 85% and decision tree has achieved the second highest accuracy which is 81%”
