## 2 Data Description
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Timeline of Visit**} \\ \hline
**Category** & **Description** & **Baseline** & **12 Months** & **24 Months** \\ \hline Subject characteristics & Anthropometric parameters including height, weight, BMI, abdominal circumference, etc. & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ Behavioural & Participants’ social behaviour and quality level of daily routine & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & Questionnaire data regarding a Participant’s arthritis-related and general health histories & \(\bullet\) & - & - \\ Medical history & and medications & & & \\ Medical imaging & Medical imaging outcomes (e.g., osteophytes and & & \\ outcome & joint space narrowing) & \(\bullet\) & - & - \\ Nutrition & Block Food & & & \\  & Frequency questionnaire & & - & - \\ Physical activity & Questionnaire results regarding leisure activities, etc. & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & Physical measurements of participants, including & & & \\ Physical exam & isometric strength, knee and hand exams, walking & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & tests and other performance measures & & & \\ Symptoms & Arthritis symptoms and general arthritis or & & & \\  & health-related function and disability & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main categories of the feature subsets considered in this paper. * Dataset A (FS1): Progressors vs. non-progressors using data from the baseline visit Input: This dataset only contains data from the baseline (724 features). After data resampling, the participants were divided into two equal categories (Figure 1), as follows:
* Class A1 (KOA progressors): This class comprises 341 participants who had KL 0 or 1 at baseline, but they had also some incident of \(\text{KL}\geq 2\) at visit 1 (12 months) or later until the end of the OAI study in at least one of the two knees or in both. * Class A2 (non-progressors): This class involves 341 participants with KL 0 or 1 at baseline, with follow-up x-rays but no incident of \(\text{KL}\geq 2\) for both of their knees until the end of the OAI study. Output: Classification outputs 0 and 1 corresponding to assignments to classes A1 and A2, respectively. * Dataset B (FS2): Progressors vs. non-progressors using progression data within the first 12 months  
Input: Dataset B contains data that declares the features' progression within the first 12 months. Specifically, the Equation (1) denotes the way that this progression was calculated. \[dx_{i,j}^{k}=\ x_{i,j}^{k}-\ x_{i,j}^{0}\ \ \,\ \forall j\in\mathcal{F} \tag{1}\]  
Figure 1: Flow chart of study design for dataset A.  
where \(x^{k}_{i,j}\) and \(x^{0}_{i,j}\) are the \(j\) components (features) of sample \(x_{i}\) measured at the visit \(k\) and the baseline (visit 0), respectively; \(dx^{k}_{i,j}\) is the calculated progression of \(x_{i,j}\) within the time period between the \(k\)-th visit and the baseline and \(\mathcal{F}\) denotes the subset of features that co-exist in both visits (233 features for dataset B). As an example, let us consider the participant \(x_{100}\) with a body mass index (\(P01 BMI\)) of 20 at the baseline visit (\(x^{0}_{100,49}=20\), where \(j=49\) is the index of feature \(P01 BMI\)). Let us also assume that the participant's BMI at visit 1 has increased to 25 (\(x^{1}_{100,49}=25\)). Thus, the BMI progression of the specific participant is calculated as \(dx^{1}_{100,49}=25-20=5\). This calculation has been performed for all the 233 features of dataset B. After data resampling, the following two classes of participants were created (Figure 2), as follows:  
* Class B1 (KOA progressors): This class comprises progression data \(dx^{1}_{i,j}\) of 268 participants who were healthy (KL 0 or 1) within the first 12 months (both at the baseline and the visit 1), but they had an incident of KL \(\geq\) 2 at the second visit (24 months) or later (until the end of the OAI study).