## 5 Discussion  
This paper focuses on the development of a ML-empowered methodology for KL grades prediction in healthy participants. The prediction task has been coped as a two-class classification problem where the participants of the study were divided into two groups (KOA progressors and non-progressors). Various ML models were employed to perform the binary classification task (KOA progressors versus non-progressors) where accuracies up to 74.07% (Dataset D) were achieved. Within the secondary objectives of the paper were to identify informative risk factors from a big pool of available features that contribute more to the classification output (KOA prediction). Moreover, we explored different options with respect to the time period within which data should be considered in order to reliably predict KOA progression. Three different options were investigated as far as the time period within which data should be considered in order to reliably predict KOA progression. To accomplish this, we worked with 5 different datasets. We first examined whether baseline data (dataset A) could solely contribute in predicting KOA progression. Going one step further, the features 'progression within the first 12 months or 24 months was also considered as an alternative source of information (datasets B and C). The aforementioned analysis in Section 4 revealed that: (i) a 71.71% prediction performance can be achieved using features from the baseline, (ii) features' progression cannot solely provide reliable KOA predictions and (iii) a combination of features is required to maximize the prediction capability of the proposed methodology. Specifically, the overall best accuracy (74.07%) was obtained by combining datasets A and B that contain features from the baseline visit along with their progression over the next 12 months. Considering a longer period of time (24 months) in the calculation of features' progression resulted to lower prediction accuracies (71.81%). The proposed FS methodology outperformed six well-known FS techniques achieving the best tradeoff between prediction accuracy and dimensionality reduction. From the pool of approximately 700 features of the OAI dataset, fifty-five were finally selected in this paper to predict KOA. As far as the nature of the selected features, it was concluded that symptoms, medical imaging outcomes, nutrition and medical history are the most important risk factors contributing considerably to the KOA prediction. However, it was also extracted that a combination of heterogeneous features coming from almost all feature categories is needed to effectively predict KL progression. Seven ML algorithms were evaluated for their suitability in implementing the prediction task. Table 7 with the summary of all reporting result indicates that LR and SVM were proved to be the best performing models. The good performance of SVM could be attributed to the fact that SVM models are particularly well suited for classifying small or medium-sized complex datasets (both in terms of data size and dimensionality). LR was the second-best performer providing the highest prediction accuracy in datasets A and B and the second highest in datasets D and E. The fact that a generalized linear model such as LR accomplishes high performances indicates that the power of the proposed methodology lies on the effective and robust mechanism of selecting important risk factors and not so much on the complexity of the finally employed classifier. Identifying important features from the pool of heterogeneous health-related parameters (including anthropometrics, medical history, exams, medical outcomes, etc.) that are available nowadays is a key to increase our understanding of the KOA progression and therefore to provide robust prediction tools. A few studies have recently addressed the problem of predicting KOA progression from different perspectives and employing different data sources. A weighted neighbor distance classifier was presented by [PERSON] et al.