# Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

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## Abstract

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## 1. Introduction

## 2. Methods

#### 2.1. Data Sources

#### 2.2. Data Extraction

#### 2.3. Data Analyses

## 3. Results

#### 3.1. Machine Learning Principles

#### 3.1.1. Input. Definition and Methods

#### 3.1.2. Output. Learning Representation

- Decision tables are one type of information tables with a decision attribute giving the decision classes for all objects [38]. Table 1 shows a simple example of a decision table, where the last column represents the final decision. In decision tables, the way of inferring the output is to make the same as the input. Because of this, decision tables are one of the simplest way of representing the output from a ML classification model.
- Decision trees (DT) are predictive representations that can be used both for classification and regression models. Decision trees are a hierarchical way of partitioning the space, where the goal is to create a model that predicts the value of a target variable based on several input variables. A DT learns by splitting the source set into subsets that are based on an attribute value test. This process is repeated on each derived subset in a recursive way, called recursive partitioning. When a DT is used for classification tasks, it is more appropriately referred to as a classification tree. On the other hand, when it is used for regression tasks, it is called regression tree [39]. Breiman et al. [40] provided a simple example of a classification tree with medical data. In this example, it was predicted that the high or low risk of patients did not survive at least 30 days based on the initial 24 h. Serrano et al. [41] proposed an analysis through a regression tree for studying the successful weight loss from a commercial health app user for three distinct subgroups: the occasional users, the basic users, and the power users of the app. In both examples, a new sample can be inferred based on the corresponding representation.
- Regression lines are the most common representations for linear regression. The regression line is that one which is the best suited to the data point cloud. Yoon et al. [42] analysed the influence of the pulse pressure on the systolic blood pressure. A new sample can be inferred throughout the regression line known as the value of the pulse pressure.
- Hyper-plane diagrams are a specific type of representations of SVM algorithms. The basic idea is to find the maximum margin hyper-plane to separate different classes clearly and maximise the distance between them [43]. Tomar and Agarwal [44] applied the hyper-plane diagram for classifying diabetic patients basing on the blood glucose level and the body mass index. A new sample can be inferred throughout the known values of the blood glucose level and the body mass index.
- Clusters are a specific type of representations of clustering algorithms. In this case, the output takes the form of a diagram showing how the instances fall into clusters. In the simplest case, this involves associating a cluster number with each instance, which might be depicted by laying the instances out in two dimensional and partitioning the space [13]. Rawte and Anuradha [45] clustered patients, depending on whether they suffered hearth diseases, arthritis, or Parkinson’s disease. In this case, a new instance cannot be inferred, since clustering tasks are only designed for organising data.

#### 3.1.3. Training and Testing

#### 3.1.4. Credibility. Algorithm Evaluation

#### 3.2. Deep Learning Principles

#### 3.3. Applications

## 4. Discussion

#### 4.1. Advantages and Disadvantages

#### 4.2. Applicability of ML to Clinical Practice

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AI | Artificial Intelligence |

AL | Active Learning |

BD | Big Data |

CIMLR | Cancer Integration via Multikernel Learning |

DBSCAN | Density-Based Spatial Clustering of Applications with Noise |

DL | Deep Learning |

DM | Data Mining |

DMD | Dynamic Mode Decomposition |

DMDC | DMD with Control |

DT | Decision Trees |

GloVe | Global Vectors |

ID3 | Iterative Dichotomiser 3 |

JCR | Journal Citation Reports |

KNN | K-Nearest-Neighbour |

LH-PCR | Length Heterogeneity Polymerase Chain Reaction |

LOO | Leave-one-out |

LPO | Leave-p-out |

ML | Machine Learning |

PCA | Principal Component Analysis |

PINS | Perturbation Clustering for Data Integration and Disease Subtyping |

POD | Proper Orthogonal Decomposition |

RF | Random Forest |

SHIMR | Sparse High-order Interaction Model with Rejection Option |

SNF | Similarity Network Fusion |

SOMS | Self-Organized Maps |

SVM | Support Vector Machine |

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Patient | Head Ache | Muscle Ache | Fever | Flu |
---|---|---|---|---|

P1 | Yes | No | Yes | Yes |

P2 | No | Yes | Yes | Yes |

P3 | Yes | No | Yes | No |

P4 | Yes | Yes | Yes | Yes |

P5 | No | Yes | Yes | Yes |

P6 | No | Yes | No | No |

Area | Author | Goal | Algorithm |
---|---|---|---|

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 | Naïve Bayes | |

[137,143] | Classification | Bayesian Networks | |

[145] | Regression | Linear regression | |

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 | Naïve 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 | |

Parkinson’s disease | [77,80] | Clustering | K-means Clustering |

[91] | Clustering | SOMS | |

[177] | Classification | KNN + SVM | |

[117,124,125] | Classification | KNN | |

[134] | Classification | Naïve 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 | |

Alzheimer’s diseases | [83] | Clustering | K-means Clustering |

[89,90] | Clustering | DBSCAN | |

[92,93,94] | Clustering | SOMS | |

[119,124] | Classification | KNN | |

[132] | Classification | Naïve Bayes | |

[138,139,140] | Classification | Bayesian Networks | |

[155] | Classification | Neural Networks + SVM | |

[157] | Classification | Neural Networks + KNN | |

[167] | Classification | SHIMR | |

[178] | Classification | Naïve Bayes + SVM + RF | |

[179] | Classification | RF | |

Heart and vascular diseases | [180] | Classification | RF |

[96,97] | Classification | SVM | |

[110,114] | Classification | ID3 | |

[115] | Classification | KNN | |

[126,127,128] | Classification | Naïve Bayes | |

[142] | Classification | Bayesian Networks | |

[148] | Regression | Linear regression | |

[181,182] | Classification | DL | |

[183] | Regression | Gradient boosting | |

[184] | Classification | KNN + RF + DT | |

Hepatic diseases | [99] | Classification | SVM |

[113] | Classification | ID3 | |

[185] | Regression | Linear regression | |

[115] | Classification | KNN | |

[129,185] | Classification | Naïve Bayes | |

[186] | Classification | Ensemble Feature Selection | |

[170] | Classification | Cross-sectional models | |

Infectious diseases | [78,82] | Clustering | K-means Clustering |

[85] | Clustering | DBSCAN | |

[72,98,101,102,103,104,105] | Classification | SVM | |

[107,111] | Classification | ID3 | |

[72,121,123] | Classification | KNN | |

[133] | Classification | Naïve Bayes | |

[71,147,148,149,150,151,153,154] | Regression | Linear regression | |

[151,154] | Regression | Logistic regression | |

[71,156] | Classification | Neural Networks + SVM | |

[165] | Classification | Random Forest | |

Renal diseases | [112] | Classification | ID3 |

[115] | Classification | KNN | |

[129] | Classification | Naïve Bayes | |

[144,148] | Regression | Linear regression | |

[144] | Regression | Logistic regression | |

[162] | Classification | Neural Networks + Naïve Bayes | |

[162,163] | Classification | Neural Networks + SVM | |

[187] | Classification | SVM | |

Other diseases | |||

Vision | [164] | Classification | Neural Networks |

Digestive | [166] | Regression | RF |

Cutaneous | [168] | Regression | GloVe |

Respiratory | [169] | Classification | SVM + RF |

Rare | [188,189] | Classification | KNN + RF + NB + DL |

Method | Advantages | Disadvantages |
---|---|---|

ML | Algorithms are often easy to be implemented. Algorithms are flexible enough to handle complex problems with multiple interacting variables. Input and output are not necessarily fixed. | Complex relationships between dependent and independent variables are not identified easily in high-dimensional databases. High computational cost. |

DL | Complex relationships between dependent and independent variables are identified easily in high-dimensional databases. Ability to handle databases with high noise. | Input and output are fixed. Overfitting problem possibility is high. Implementation is not so easy than in ML. Training process requires a higher computational cost than ML. |

Method | Advantages | Disadvantages |
---|---|---|

Unsupervised learning | It does not require a training data to be labelled. The automatic labelling of the training data set saving the time spent in hand classification. Classification task is fast. | There are no notions of the output along the learning process. It does not allow to estimate or map the results of a new sample. Results vary considerably in the presence of outliers. It only performs classification tasks. |

Supervised learning | It exists notions of the output along the learning process. It performs classification and regression tasks. It allows estimating or mapping the results to a new sample. | It requires a labelled data set. It requires a training process. |

Method | Advantages | Disadvantages |
---|---|---|

K-means Clustering | Simple clustering approach. Efficient clustering method. Method is easy to be implemented. | Requires a number of clusters in advance. Handling categorical attributes cause problems. Results vary considerably in the presence of outliers. |

DBSCAN/SOMS | Simple clustering approach. A number of clusters in advance is not required. Efficient clustering method. | Handling categorical attributes cause problems. Results vary considerably in the presence of outliers. |

SVM | Better accuracy compared to other classifiers. Overfitting problem is not so great as in other methods. | High computational cost. Training process requires more time than other methods. |

ID3 | There are no domain requirements. Exact value results are provided for various actions, minimising the ambiguity of complex decisions. High dimensional databases are processed easily. Classifier and output are easy to be interpreted. | Results are restricted to one output attribute. Only categorical output is generated. Classifier performance depends on the type of dataset, making it unstable. |

KNN | Method is easy to be implemented. Training process requires low computational cost. | Large storage space is required. Sensitivity to databases with high noise. Testing process requires high computational cost. |

Naïve Bayes Bayesian Networks | Method is easy to be implemented. Method is speeder and provide more accuracy in high dimensional databases than other methods. | Low accuracy is provided in cases where exists dependence between variables. |

Linear regression | Better accuracy compared to other classifiers. Complex relationships between dependent and independent variables are identified easily. | Results vary considerably in the presence of outliers. Training process requires more time than other methods. Classifier performance depends on the type of dataset, making it unstable. Only numerical output is generated. |

Logistic regression | Better accuracy compared to other classifiers. Complex relationships between dependent and independent variables are identified easily. | Results vary considerably in the presence of outliers. Training process requires more time than other methods. Classifier performance depends on the type of dataset, making it unstable. Only categorical output is generated. |

Neural network | Complex relationships between dependent and independent variables are identified easily. Ability to handle databases with high noise. A previous feature extraction task is not required. | High possibility of local minima. High possibility of overfitting problem. Classifier is difficult to be interpreted. High computational time is required if there is a large number of layers. No explanation or justification of decisions can be given, i.e., a “black box” characteristic. |

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## Share and Cite

**MDPI and ACS Style**

Caballé-Cervigón, N.; Castillo-Sequera, J.L.; Gómez-Pulido, J.A.; Gómez-Pulido, J.M.; Polo-Luque, M.L.
Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. *Appl. Sci.* **2020**, *10*, 5135.
https://doi.org/10.3390/app10155135

**AMA Style**

Caballé-Cervigón N, Castillo-Sequera JL, Gómez-Pulido JA, Gómez-Pulido JM, Polo-Luque ML.
Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. *Applied Sciences*. 2020; 10(15):5135.
https://doi.org/10.3390/app10155135

**Chicago/Turabian Style**

Caballé-Cervigón, Nuria, José L. Castillo-Sequera, Juan A. Gómez-Pulido, José M. Gómez-Pulido, and María L. Polo-Luque.
2020. "Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review" *Applied Sciences* 10, no. 15: 5135.
https://doi.org/10.3390/app10155135