Diabetes prediction using machine learning and explainable AI techniques

Abstract

Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.

Keywords: AdaBoost; K‐nearest neighbour; android Application; decision tree; diabetes; random forest; support vector machine.

FIGURE 1

Working sequences of the proposed diabetes prediction system

FIGURE 2

Percentage of people having diabetes in the Pima Indian dataset

FIGURE 3

Feature importance hierarchy

FIGURE 4

Working steps of predicting insulin of the RTML dataset

FIGURE 5

Development of the web application

FIGURE 6

Working sequences of the proposed android application development

FIGURE 7

Confusion matrix for XGBoost with ADASYN technique

FIGURE 8

ROC curve and AUC value for the XGBoost with ADASYN

FIGURE 9

Explainable AI interpretation of feature importance of XGBoost with ADASYN

FIGURE 10

LIME explainable AI prediction interpretation

FIGURE 11

Instantaneous diabetes prediction by the designed web application

FIGURE 12

Home screen of the proposed android application

FIGURE 13

Android application review ratings