## 3 Results
### Applications  
Through the application of the ML and DL basic methods described in the previous section, different authors discovered interesting patterns in the medical field, helping them, in some cases, in the decision-making. As particular case of interest nowadays, ML means an useful approach in developing diagnostic models supported by clinical data in the context of current Corona Virus Disease 2019 (COVID-19) pandemic. [PERSON] et al. [68] and [PERSON] [69] discuss about this opportunity. Many examples of ML and DM applications support this approach, as [PERSON] et al. [70] show in a deep systematic review [70]. We point out just few representative cases. [PERSON] et al. [71] present a model that is able to predict the spread of COVID-19 by performing linear regression, multilayer perceptron, and vector autoregression methods. [PERSON] et al. [72] apply a supervised ML-based alignment approach on an intrinsic COVID-19 virus genomic signature for classifying the whole COVID-19 virus genomes. Even in the war against misinformation about COVID-19 content, ML is able to quantify the online opponents of establishment health guidance, as [PERSON] et al. shows [73]. Next, we analyse some examples of ML applications in other heterogeneous medical fields. Starting with the unsupervised algorithm of ML K-means Clustering, DBSCAN, SOMS, SNF, and PINS. Based on K-means Clustering, [PERSON] et al. [74] identified gene clusters overlapping between immune and autoimmune human diseases. [PERSON] et al. [75] assigned tumour classes to subclasses to recognise the dominant feature of the gene list characterising each subclass. [PERSON] et al. [76] segmented medical images to provide important information from 2-D magnetic resonance images on the head. [PERSON] et al. [77] analysed inter-relationship between neuropsychiatric symptoms in patients with Parkinson's disease and dementia. [PERSON] et al. [78] screened unknown and unexpected infectious diseases. [PERSON] et al. [79] classified the genomic response between human inflammatory diseases. [PERSON] et al. [80] classified the Plasmodium species in thin blood smear images to control malarial infection disease. [PERSON] and [PERSON] [81] classified mammographic images, reducing the false positive results that are generated by other methods. [PERSON] [82] identified characteristics of patients suffering tuberculosis infectious disease and [PERSON] et al. [83] segmented magnetic resonance images to diagnose different brain disorders, such as Alzheimer's disease. Based on DBSCAN, [PERSON] et al. [84] clustered lung, kidney, throat, stomach, and liver cancer in databases. [PERSON] et al. [85] detected anomalies in temperature data. [PERSON] and [PERSON] [86] diagnosed prostate cancer through transrectal ultrasound images. [PERSON] et al. [87] identified the examination pathways commonly followed by patients with diabetic disease. [PERSON] [16; 88] diagnosed [PERSON]'s disease from the voice dataset. [PERSON] et al. [89] and [PERSON] and [PERSON] [90] discriminated [PERSON]'s disease while using magnetic resonance imaging data features and longitudinal information, respectively. Based on SOMS, [PERSON] et al. [91] identified [PERSON]'s disease. [PERSON] et al. [92], [PERSON] et al. [93], and [PERSON] et al. [94] clustered [PERSON]'s disease. Based on SNF, [PERSON] et al. [24] combine mRNA expression, DNA methylation, and microRNA (miRNA) expression data for five cancer data sets, outperforming single data type analysis and established integrative approaches when identifying cancer subtypes and it is effective for predicting survival. Based on PINS, [PERSON] et al. identify known cancer subtypes and novel subgroups of patients with significantly different survival profiles [25]. Based on CIMLR, [PERSON] et al. extract biologically meaningful cancer subtypes from multi-omic data from 36 cancer types [26]. Within the supervised algorithms of ML, we provide some application examples of SVM, ID3, KNN, Naive Bayes, Bayesian Networks, and linear and logistic regression algorithms.