Comparative analysis of machine learning algorithms for early prediction of diabetes mellitus in women

Diabetes is a chronic disease characterized by hyperglycemia where a person suffers from a high level of blood sugar, which leads to complications such as blindness, cardiovascular diseases, and amputation. It is expected that in 2040 the diabetic patients will reach 642 million globally. Hence considering this alarming figure there is a strong need to early diagnose and predict the symptoms of diabetes to save precious human lives. One possible way to diagnose this disease is to leverage machine learning algorithms. Machine learning has swiftly been infiltrating in various domains in healthcare. With the help of diabetes data, machine learning algorithms can find hidden patterns to predict whether a patient is diabetic or non-diabetic. This research aims to provide a comparative analysis of the performance and effectiveness of selected machine learning algorithms in predicting diabetes in women. We develop a predication framework and implemented ten different machine learning algorithms, namely: Naive Bayes, BayesNet, Decision Tree, Random Forest, AdaBoost, Bagging, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Multi-Layer Perceptron. Experimental results procured for the Frankfurt hospital (Germany) dataset shows that K-Nearest Neighbor, Random Forest, and Decision Tree outperformed the other algorithms in terms of all metrics. We believe that our diabetes prediction framework will assist doctors to predict diabetes mellitus with high accuracy.