## 3 Results
### Applications
tackle diabetic retinopathy and age-related macular degeneration by means of convolution neural networks [164]. There are many other ML methods that are applied for classification, prediction or clustering purposes in all kind of diseases. For example, [PERSON] et al. apply Random Forest (RF) to identify genes associated with invasive disease in Streptococcus pneumoniae [165]. This technique was also applied by [PERSON] et al. To identify the incidence of hyperglyaemia in steroid treated hospitalised inflammatory bowel disease [166]. [PERSON] et al. apply a ML model called Sparse High-Order Interaction Model with Rejection Option (SHIMR) for diagnosis of [PERSON]'s disease [167]. [PERSON] et al. predict drug repurposing for immune-mediated cutaneous diseases by applying Global Vectors (GloVe), an unsupervised ML algorithm for obtaining vector representations for words [168]. [PERSON] et al. apply Proper Orthogonal Decomposition (POD), Principal Component Analysis (PCA), Dynamic Mode Decomposition (DMD), and DMD with Control (DMDC) to diagnose obstructive lung diseases using exhaled aerosol images, as well as they classify image data using both the SVM and RF algorithms [169]. Finally, [PERSON] et al. consider Cross-sectional (CS) and longitudinal models as predictors in chronic hepatitis C virus [170]. Table 2 shows a summary of all these works, focusing on the goal searched, the algorithm used, and the area of application within the medical field. Figure 3 shows the same information than Table 2, although it counts the number of references instead of identifying them. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Area** & **Author** & **Goal** & **Algorithm** \\ \hline Metabolic diseases & [74; 79] & Clustering & K-means Clustering \\  & [87] & Clustering & DBSCAN \\  & [171] & Regression & Random Forest \\  & [100] & Classification & SVM \\  & [106; 109] & Classification & ID3 \\  & [115; 116; 118; 120; 122] & Classification & KNN \\  & [135] & Classification & Naive Bayes \\  & [137; 143] & Classification & Bayesian Networks \\  & [145] & Regression & Linear regression \\ \hline Cancer & [75; 81] & Clustering & K-means Clustering \\  & [84; 86] & Clustering & DBSCAN \\  & [24] & Clustering & SNF \\  & [25] & Clustering & PINS \\  & [26] & Clustering & CIMLR \\  & [95; 172] & Classification & SVM \\  & [108] & Classification & ID3 \\  & [130] & Classification & Naive Bayes \\  & [136] & Classification & Bayesian Networks \\  & [148; 173] & Regression & Linear regression \\  & [146; 174] & Regression & Logistic regression \\  & [157] & Classification & Neural Networks + KNN \\  & [156] & Classification & Neural Networks + SVM \\  & [160] & Classification & Neural Networks + ID3 \\  & [161] & Classification & KNN \\  & [175] & Classification & DT \\  & [176] & Classification & DL \\ \hline Parkinson’s disease & [77; 80] & Clustering & K-means Clustering \\  & [91] & Clustering & SOMS \\  & [177] & Classification & KNN + SVM \\  & [117; 124; 125] & Classification & KNN \\  & [134] & Classification & Naive Bayes \\  & [141] & Classification & Bayesian Networks \\  & [152] & Regression & Linear regression \\  & [125; 152] & Regression & Logistic regression \\  & [155; 159] & Classification & Neural Networks + SVM \\  & [125; 158] & Classification & Neural Networks + KNN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Application summary of representative techniques.