Hyper Parameter Tuned Ensemble Approach for Gestational Diabetes Prediction

Abstract

Diabetes Mellitus is commonly found in human beings around the world and this is one of the serious diseases which causes boundless suffering among patients. There are numerous reasons for the prevalence of this disease. It would be better to consider the predictions carried out earlier in this respect, since diabetes is a non - communicable disease and makes a great impact on the health condition of people nowadays. This is the reason why the existing medicinal practices in most hospitals are collecting patients' life history or the record of the disease. This is done for diagnosing diabetes using various medical tests followed by proper treatment for the disease. Machine learning provides an immense contribution to the sector of healthcare. For this research, Pima Indians Diabetes Dataset, obtained from the University of California, Irvine (UCI) machine learning source that included 768 patients' details along with nine attributes had been chosen for a comprehensive investigation of this grave and widespread problem in the health sector. Eventually, an adequate perfect outcome could be achieved and some effective and transparent conclusions were made. Among 768 diabetics, 500 were recognized as positive for the disease while 268 were recognized as negative. Besides, the recorded facts were put into particular supervised machine learning techniques such as Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA), Logistic Regression (LR) and k-nearest neighbors (k-NN). Along with this, bagging and boosting techniques like Random Forest (RF), Extreme Gradient Boosting (XGBoost), LightGBM, and CatBoost too were taken into consideration. In addition, by considering classifiers with the highest accuracies, the final ensemble model was developed with the adaption of SVM, CatBoost and RF to predict the diabetes mellitus. Thus, the model resulted in an accuracy of 86.15%.