## 4 Discussion
The proposed algorithm has also been assessed on a real case study, in which the aim was to rank risk factors for T2D onset and to develop a predictive model of T2D onset that takes into account the most important ones. The dataset included more than 6000 subjects, monitored for up to 15 years within a longitudinal study of health conducted in U.K. To test the ranking performance of the proposed algorithm, we included two highly correlated features in the set of candidate predictors (BMI and waist circumference) related to obesity, which is well recognized as the leading risk factor for T2D onset [29]. Nevertheless, the standard RFE-Borda count method failed to estimate with high confidence the rank of these obesity variables, because the high correlation between the two features altered the ranking results. Conversely, the proposed algorithm assigned to the obesity variables the highest importance level, and it was able to estimate their rank with high confidence. This proves what was already verified with simulated data: the proposed algorithm is able to robustly perform variable ranking in the presence of highly correlated features, outperforming the standard RFE-Borda count method. The developed T2D model included 8 variables: BMI or waist, HDL cholesterol, systolic blood pressure, self-reported poor health level, depression scale, the level of deprivation, history of hypertension, and sex or age. While variables related to obesity, hypertension and hypercholesterolemia are commonly used by literature T2D models [22], a few models considered socio-economic factors, such as deprivation [31] or income [35]. As far as depression is concerned, this variable has never been used in literature T2D models. Nevertheless, several studies found an association between depression symptoms and the incidence of T2D [36]. Our results support this association, as depression was ranked in fifth position by the variable ranking algorithm, immediately after three strong risk factors (i.e., obesity, cholesterol, and hypertension) and the self-reported poor health level. Regarding the discrimination ability, the developed T2D model achieved comparable performance to that of other literature models assessed on the same dataset [3], e.g., the FINDRISC model [26], the basic model by [PERSON] et al. [37], and the Atherosclerosis Risk in Communities simple model [38], with a C-index of around 0.75. Other literature models were able to achieve a higher discrimination performance (e.g., C-index > 0.80) by considering among the predictors variables related to dysglycermia, such as fasting plasma glucose, results of the oral glucose tolerance test, and the homeostatic model assessment of insulin resistance and beta cell indices [3; 30; 38; 22]. Although promising results were achieved, the study conducted in this paper presents some limitations that deserve future investigation. A limitation of the proposed algorithm is that it can only deal with highly correlated numerical variables. In future works, the algorithm needs to be extended to deal also with categorical variables. This can be achieved by replacing the correlation coefficient with a suitable measure of the association between categorical variables, such as the Cramer's V, the phi coefficient, or the mutual information [39]. Other limitations concern the validation procedure. In this paper, the proposed variable ranking algorithm was assessed considering the Cox proportional hazard model as a base model, which was the simplest and most natural approach to address the prediction problem of the presented case study. However, the proposed algorithm is general and can be applied in principle to any ML model. In future works, the proposed algorithm could be tested with different models, including linear and non-linear regression and classification models. Finally, we recognize that the in-silico assessment performed in this study makes several assumptions.