Predictive Model and Feature Importance for Early Detection of Type II Diabetes Mellitus

Abstract

BackgroundAccurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. MethodsThe study involves 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, fasting blood sugar (FBS), serum lipids [(total cholesterol (TC), triglycerides (TG), high and low-density lipoprotein cholesterol (HDL-c and LDL-c)] were collected. From this data, four ML classification algorithms including Naïve-Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Decision Tree (DT) were used to predict T2DM. Precision, Recall, F1-Scores, Receiver Operating Characteristics (ROC) scores and the confusion matrix were computed to determine the performance of the various algorithms while the importance of the feature attributes was determined by recursive feature elimination technique.ResultsAll the classifiers performed beyond the acceptable threshold of 70% for the Precision, Recall, F-score and Accuracy. After building the predictive model, 82% of diabetic test data was detected by the NB classifier, of which 93% were accurately predicted. The SVM classifier was the second-best performing classifier which yielded an overall accuracy of 84%. The non-T2DM test data yielded an accurate prediction score of 75% from the 98% of the proportion of the non-T2DM test data. KNN and DT yielded accuracies of 83% and 81%, respectively. NB has the best performance (AUC=0.87) followed by SVM (AUC= 0.84), KNN (AUC= 0.85) and DT (AUC= 0.81). The best three feature attributes, in order of importance, are HbA1c, TC and BMI whereas the least three importance of the features are Age, HDL-c and LDL-c.ConclusionBased on the predictive performance and high accuracy, the study has shown the potential of ML as a robust forecasting tool for T2DM. Our results can be a benchmark for guiding policy decisions in T2DM surveillance in resource and medical expertise limited countries such as Ghana.

Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus

“…EAY 3 and EA 1,2 conceived the study. EAY 3 performed statistical analyses and interpretated the results.…”

“…Type II diabetes mellitus (T2DM), characterised by persistent rise in plasma glucose 1 , 2 , may have existed over two centuries ago. At the time, the disease could kill within weeks or months of diagnosis 3 .…”

“…It is a public knowledge that effective biomarkers can promote early detection, which in turn, can stimulate a timely intervention, and delay the onset of T2DM 2 , 4 . However, efforts to obtain robust biomarkers for the condition have been hindered by the complex nature of the condition.…”

Multi-Block Data Integration Analysis for Identifying and Validating Targeted N-Glycans as Biomarkers for Type II Diabetes Mellitus

“…EA 1,2 and EAY 3 wrote the manuscript. EPY 4 , KAA, EA 2 , EOA 2,5 and WW 6 edited the ideas and concepts presented. All authors read and approved the final manuscript.…”

“…It is a public knowledge that effective biomarkers can promote early detection, which in turn, can stimulate a timely intervention, and delay the onset of T2DM 2,4 . However, efforts to obtain robust biomarkers for the condition have been frustrated by the complex nature of the condition.…”