## 5 Machine Learning Implementation  
After extracting signal features, machine learning (ML) algorithms were applied to classify the collected data into gender, as well as into age, BMI, and CWP ranges. For our analysis, we chose to employ Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest algorithms. The decision to utilize these traditional ML algorithms instead of deep learning models was based on several considerations. Firstly, deep learning models typically require large amounts of data to perform optimally and avoid overfitting [47]. Given the size and complexity of our dataset, SVM, KNN, and Random Forest are more suitable as they can yield robust results with smaller datasets [48]. Secondly, these algorithms offer greater interpretability compared to deep learning models. They allow for an understanding of the importance of different features and their contribution to the final prediction. This aspect is crucial in our study, which aims to understand the impact of physiological and body stature variations on signals acquired by the Bio-Radar [49]. Thirdly, SVM, KNN, and Random Forest are less resource intensive and can be trained more quickly than deep learning models. This is a significant advantage when working with limited computational resources [50]. Lastly, in many scenarios, traditional machine learning algorithms such as SVM, KNN, and Random Forest can achieve performance comparable to those of deep learning algorithms, particularly when the data are not excessively complex or large in volume [51]. Therefore, given the nature of our data and the specific objectives of this research, SVM, KNN, and Random Forest emerged as the most appropriate algorithms for this study. SVM is a non-parametric classification technique based on statistical learning theory [52]. The main goal of SVM is to find the hyperplane separating the data such that the distance from this hyperplane to each data class is as large as possible. The KNN classifies a new observation by measuring the distance of this observation from its nearest points and then categorizing it in the class to which most of the K points belong. A higher number of K neighbors will imply a smoother classification boundary. On the other hand, a small number of K produces a more flexible boundary with a high variance but low bias [53]. The last classifier, Random Forest, is used in regression and classification problems. A Random Forest consists of a combination of tree predictors. Each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them [54]. The final result of this model is the result returned from the trees with the highest vote, or the average of these. The data resulting from Section 3 are split into training, test, and new subject categories. This dataset is composed of 92 individuals that have nine 1-minute signal windows. Two scenarios regarding this division were examined: in the first, two individuals were designated as new data, and in the second, five individuals were designated as new data. In both scenarios, the remaining participants were used for training and testing, following a 70:30 hold-out strategy. This selection process is illustrated in Figure 11. The same procedure was repeated over twenty iterations. Figure 12 depicts the implemented process. In this study, four different combinations of training-test models were conducted to identify the conditions that would yield the best results:  
* Correlation value of 0.7 with 2 people as new data;
* Correlation value of 0.7 with 5 people as new data;  
This division was strategically chosen for several reasons, which we elucidate here to provide clarity on our methodology. Firstly, the division into two cases allowed us to assess the model's adaptability and performance with varying degrees of data variability.