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Noncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide. Cardiovascular diseases (CVDs) and diabetes are the most prevalent NCDs, causing 1.9 and 1.5 million deaths yearly. Individuals diagnosed with type 1 diabetes (T1D) are at high risk of developing CVDs. Machine learning (ML) models have provided outstanding results in different domains, including healthcare, allowing to obtain models with high predictive performance. The aim of this study was to develop an interpretable data-driven approach to predict the 10-year CVD risk for T1D older individuals, aiming to provide both reasonable predictive performance and the identification of risk factors associated with CVDs. Data from T1D individuals at the Steno Diabetes Center Copenhagen were used. Different ML-based models were considered, including KNN, decision tree, random forest, and multilayer perceptron (MLP). To enhance the predictive performance of ML models, the conditional tabular generative adversarial network (CTGAN) was used to create synthetic data and increase the size of the training data. Several filter and wrapper feature selection (FS) techniques were considered for identifying the most relevant features involved in CVD risk and enhancing the performance of the ML-based models used. To gain interpretability on predictive models, we used the post-hoc methods: SHAP and accumulated local effects. The experimental results showed a great performance of FS and ML-based models for predicting CVD risk. In particular, the MLP obtained the best results, with a mean absolute error of 0.0088 and mean relative absolute error of 0.0817. Regarding risk factors, age, Hba1c, and albuminuria were identified as crucial in CVD risk prediction, which is in line with recent clinical evidence. Our study contributes to identifying CVD risk and associated risk factors in a data-driven manner, helping to make early interventions and adequate treatments to prevent CVDs.

Hypertension and diabetes have become a global health and economic issue, being among the major chronic conditions worldwide, particularly in developed countries. To face this global problem, a better knowledge about these diseases becomes crucial to characterize chronic patients. Our aim is two-fold: (1) to provide an efficient visual tool for identifying clinical patterns in high-dimensional data; and (2) to characterize the patient health-status through a data-driven approach using electronic health records of healthy, hypertensive and diabetic populations. We propose a two-stage methodology that uses diagnosis and drug codes of healthy and chronic patients associated to the University Hospital of Fuenlabrada in Spain. The first stage applies the Self-Organizing Map on the aforementioned data to get a set of prototype patients which are projected onto a grid of nodes. Each node has associated a prototype patient that captures relationships among clinical characteristics. In the second stage, clustering methods are applied on the prototype patients to find groups of patients with a similar health-status. Clusters with distinctive patterns linked to chronic conditions were found, being the most remarkable highlights: a cluster of pregnant women emerged among the hypertensive population, and two clusters of diabetic individuals with significant differences in drug-therapy (insulin and non-insulin dependant). The proposed methodology showed to be effective to explore relationships within clinical data and to find patterns related to diabetes and hypertension in a visual way. Our methodology raises as a suitable alternative for building appropriate clinical groups, becoming a promising approach to be applied to any population due to its data-driven philosophy. A thorough analysis of these groups could spawn new and fruitful findings.