A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.

The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of supervised methods, which on the other hand use only the labeled data during the training phase. Both the absence of automated mechanisms that produce labeled data and the high cost of needed human effort for completing the procedure of labelization in several scientific domains rise the need for semisupervised methods which counterbalance this phenomenon. In this work, a self-trained Logistic Model Trees (LMT) algorithm is presented, which combines the characteristics of Logistic Trees under the scenario of poor available labeled data. We performed an in depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique had better accuracy in most cases.