Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait

Abstract

Objective: In recent decades, the Arab population has experienced an increase in the prevalence of type 2 diabetes (T2DM), particularly within the Gulf Cooperation Council. In this context, early intervention programmes rely on an ability to identify individuals at risk of T2DM. We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline).Design: This retrospective cohort study used three models based on LR, k-nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. The models included the following baseline non-invasive parameters: age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and T2DM.Setting: This study was based on data from the Kuwait Health Network (KHN), which integrated primary health and hospital laboratory data into a single system.Participants: The study included 1,837 native Kuwaiti Arab individuals (equal proportion of men and women) with mean age as 59.5 ± 11.4 years. Among them, 647 developed T2DM within 7 years of the baseline non-invasive measurements.Analytical methods: The discriminatory power of each model for classifying people at risk of T2DM within 3, 5, or 7 years and the area under the receiver operating characteristic curve (AUC) were determined.Outcome measures: Onset of T2DM at 3, 5, and 7 years.Results: The k-NN machine-learning technique, which yielded AUC values of 0.83, 0.82, and 0.79 for 3-, 5-, and 7-year prediction horizons, respectively, outperformed the most commonly used LR method and other previously reported methods. Comparable results were achieved using the SVM and LR models with corresponding AUC values of (SVM: 0.73, LR: 0.74), (SVM: 0.68, LR: 0.72), and (SVM: 0.71, LR: 0.70) for 3-, 5-, and 7-year prediction horizons, respectively. For all models, the discriminatory power decreased as the prediction horizon increased from 3 to 7 years.Conclusions: Machine-learning techniques represent a useful addition to the commonly reported LR technique. Our prognostic models for the future risk of T2DM could be used to plan and implement early prevention programmes for at risk groups in the Arab population.

Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

“…The KELM-HAFPSO model proposed has been assessed for Accuracy, Sensitivity, Specificity, MCC, and KS by means of 5-fold crossvalidation on the basis of two related datasets. Comparative Analysis was conducted between the proposed KELM-HAFSO with the other five competitive methods namely ELM-GA [43], Decision Tree C4.5-PSO [27], k-NN [22], MLP [21], LR [17], SVM [38] and NB [16].…”

“…This study evaluated eleven risk factors that are closely related to this disease. In literature, Bassam Farran et al[22] combined FLDA) for the discovered attributes. Han Wu et al[28] used K-Means and LR algorithms to implement a data mining technique for the prediction of Type 2 diabetes mellitus.…”

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

“…Bassam et al [20] performed a study on data obtained from the Kuwait Health Network (KHN) to build prognostic models to predict the future risk of diabetes (type II) using machine learning algorithms (logistic regression, k-nearest neighbor (KNN), support vector machine (SVM)) with a five-fold cross-validation technique. The study included age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and diabetes (type II) as baseline non-invasive parameters.…”

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

“…ML is becoming a popular and efficient approach to evaluate multidimensional longitudinal health data in different fields of medical research. Examples of this kind of studies include the diagnosis of asymptomatic liver disease [16], the prediction of opioid dependence [17], the evaluation of sociodemographic determinants of health status in aging [18], the prediction of the mobility of medical rescue-vehicles [19], forecasting adverse perioperative outcomes [20], the measure of caloric intake at the population level [21], the personalisation of oncological treatment in radiogenomics [22], the determination of features of systolic blood pressure variability [23], the identification of clinical variables in bipolar disorder [24] and, interestingly, a specific interest in uncovering potential predictors of diabetes (type 1 and 2) using large set of data [25][26][27][28][29][30][31][32]. ML can also support global efforts in various fields of epidemic outbreaks of infectious diseases, developing up-to-date text and data-mining techniques to assist COVID-19-related research, especially by developing drugs faster (screening and detecting antibody virus interactions and detect viral antigens), understanding viruses better, mapping where viruses come from, and hopefully predicting the next pandemic [33,34].…”