# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Machine Learning Algorithm Selection in Human Digital Twins
overview of recent developments in precision medicine, an essential aspect of personalized healthcare, using USL. In Table 7, the algorithms that are evaluated in the examples given in Table 6 are listed. The source of the model indicates whether a single model was generated based on the entire population (Group), multiple models were generated based on data from individual participants (Individual), or multiple models were generated based on clustering or a specific selection criterion (Other). The selection of algorithms in the listed articles is, in most cases, based on general guidelines or expected potential to perform well. The conclusions in most articles are limited to presenting the algorithm that has the best performance for the given use case, without providing insight into the underlying reasons. Only [53; 76] compare a wider selection of algorithms. Whereas the conclusions in [76] are restricted to the presented use-case, ref. [53] does reflect on the results. This study demonstrates that tree-based models yield more accurate predictions for context-aware smartphone usage models. In comparison, neural network-based models do not achieve the same prediction accuracy. One of the reasons suggested in this article is the limited number of samples in individual phone usage data. Apart from our work in [21], no studies were found that compare algorithm selection or performance using individual and group models. However, in their discussion on the use of clustering techniques to generate labels used for classification in anomaly detection, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Algorithms**} & \multicolumn{1}{c}{**Model Source**} \\ \hline Anomaly Detection & [64] & CNN. & Group \\  & [70] & Wide range of classification & N/A \\ \hline \multirow{3}{*}{Classification} & [21] & ADA, DTC, KNN,LR, NN, & \multirow{3}{*}{Group and Individual} \\  & [77] & RF, SGD, SVC. & \\  & [77] & CNN, RF. & Group \\  & [48] & NB, KNN, RF. & Group \\  & [53] & ZeroR, NB, DTC, RF, SVM, & \\  & [53] & KNN, ADA, LR, RIPPER, & \\  & & RIDOR, ANN. & \\  & [42] & RF, CNN, K-Means. & \\  & [11] & KNN, SVM. & Individual \\  & [56] & RF, LR, SVM, C5.0. & \\  & [76] & CNN, LSTM, MLP, SVC, LR. & Group \\  & [73] & LR. & 4 Age Groups \\  & [65] & Bagged trees, KNN, SVM & \\  & [Linear and Cubic). & & Group \\ \hline \multirow{3}{*}{Clustering} & [49] & Wide range of unsupervised methods. & N/A \\  & [75] & K-Means. & Group \\ \hline \multirow{3}{*}{Clustering and Classification} & [74] & K-Means, followed by RF & Group and 3 Clusters \\  & [78] & Manual clustering and RF. & 3 Clusters \\ \hline \multirow{3}{*}{Regression} & [68] & SARIMAX, LSTM, GRU, & \multirow{3}{*}{Group} \\  & [66] & Transformer. & \\  & [66] & LR, DTR, RFR, GBA. & Group \\ \hline \multirow{3}{*}{Reinforcement Learning} & [72] & Q-learning, LSPI, combined & Group, Clusters, and \\  & & with K-medoids clustering. & Individual \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Application objective, algorithms, and personalization level. [PERSON] et al. [49] state that _Patient-specific ECG classifiers--trained classifiers that are fine-tuned over the ECG of the given patient--have shown superior performance over classifiers trained on a common ECG pool_, confirm our findings. In conclusion, we can say that, based on the type of problem, a first selection of applicable machine learning models and algorithms can be made. Together with the learning model paradigm that must be chosen based on the availability of labels and information in the data, as described in Table 6, a complete classification of ML algorithms can be achieved. This classification then provides possible choices for algorithm selection, which can be refined by considering whether the models are generated using group data or individual data.