Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis
(This article belongs to the Section Pediatric Nursing)
Abstract
:1. Introduction
Application of AI and IoT in Early Intervention in CATs and CDIATs
- To analyse the functionality of using supervised machine learning techniques for prediction in the group amenable to early care;
- To analyse the functionality of using supervised machine learning techniques for classification in the group amenable to early care;
- To analyse the functionality of using unsupervised machine learning clustering techniques in the group amenable to early care.
2. Materials and Methods
2.1. Design
2.2. Participants
2.3. Instruments
2.4. Procedure
2.5. Data Analysis
3. Results
3.1. Analysing the Functionality of Using Supervised Machine Learning Techniques for Prediction in the Group Amenable to Early Care
3.2. Analysing the Functionality of Using Supervised Machine Learning Techniques for Classification in the Group Amenable to Early Care
3.3. Analysing the Functionality of Using Unsupervised Machine Learning Clustering Techniques in the Group Amenable to Early Care
4. Discussion
4.1. Limitations of this Study
4.2. Future Lines of Intervention
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Techniques | Algorithms That Apply | Use in Health Sciences | Techniques |
---|---|---|---|
Supervised ML Techniques | |||
Classification | |||
SVM | Based on Vapnik’s theory [21]. In two linearly separated classes, the best boundary between two classes is searched. In non-linear functions, the kernel trick is applied. | Facilitates visual analysis | |
Discriminant Analysis | Describe whether there are significant differences in x groups and variables. It is a prediction model of a categorical response variable, x, from y classifier variables that are usually continuous. | Detection of proxy variables to adjust a diagnosis or best treatment | Facilitates visual analysis |
Nearest Neighbour | A non-parametric classification and regression method that estimates the probability density function that an element, x, belongs to a class, Ci. It is known as a lazy technique. The most frequently applied distance is Euclidean. | Can help in grouping types of patients within a pathology in relation to the degree of involvement. May also help in pooling the effectiveness of different types of treatment in different types of patients. | Facilitates visual analysis |
Decision Tree | An algorithm that detects the influence of a series of variables (independent) on other variables (dependent) in a hierarchical order. It is quick to build, interpretable, and sensitive to small changes. It can construct multiclassifiers [22] and regressors [23]. | Can detect the most effective treatment among the possible treatments or the part of a treatment in a hierarchical order of percentage explained with respect to the results of an intervention. | Facilitates a visual analysis that is highly intuitive. |
Neural Networks | Computational models that emulate human neural functioning. They include: Multi-layer Perceptron NN. Can solve problems that are not linearly separable. Outputs can be imprecise. Radial-Based Neural Networks. They can construct linear and non-linear approximations. | Used to solve problems of pattern association, image segmentation or data understanding. Time series analysis, image processing, speech recognition, and diagnostics. | Facilitates visual analysis |
Prediction | |||
Linear regression | A model used to approximate the relationship between continuous variables, a dependent variable, other independent variables, and an error variable. | Can help predict the effect of a risk factor on the development of a pathology or the effect of a type of treatment on the symptoms and progression of a pathology. Applied to variables that are measured on an interval or ratio scale. | Facilitates visual analysis |
Logistic regression | A similar model to linear regression that predicts the outcome of a categorical variable with respect to predictor variables. It examines binomially distributed data. | Can help predict the effect of a risk factor on the development of a pathology or the effect of a type of treatment on the symptoms and progression of a pathology. Applied to variables that are measured on a dichotomous scale. | Facilitates visual analysis |
Unsupervised ML techniques | |||
Clustering | |||
k-means | Allows the assignment of an element to a cluster without applying a prior clustering variable. The assignment is made with the closest distance to the centre of that cluster. The disadvantage is that the algorithm tends to form groups of similar sizes. | Allows the determination of groupings of patients without a previously defined independent variable with respect to different measurements in different relevant parameters. | It facilitates a visual analysis that is highly intuitive in this case. |
k-means ++ | Solve the k-means NP hard problem. To do so, apply a polynomial transformation and then run the cluster centre assignment algorithm. | Allows for a tighter distribution of the grouping of patients without a pre-defined independent variable for different measurements on different relevant parameters. | Facilitates a visual analysis that is highly intuitive in this case. |
Study Objectives | Data Analysis Tests |
---|---|
1. To analyse the functionality of using supervised machine learning techniques for prediction. | Supervised machine learning prediction technique: linear regression |
2. To analyse the functionality of using supervised machine learning for classification. | Supervised machine learning technique for classification: decision tree (CHAID algorithm) Cross-tabulation table |
3. To analyse the functionality of using unsupervised machine learning clustering techniques. | Unsupervised machine learning technique: k-means clustering; hierarchical clustering cross-tabulation table principal component analysis (PCA) (elbow method). |
Group | Node | |||
---|---|---|---|---|
1 | % | 2 | % | |
n = 53 | n = 60 | |||
1 | 35 | 30.97 | 14 | 12.39 |
2 | 18 | 15.93 | 46 | 40.71 |
Total | 53 | 46.90 | 60 | 53.10 |
FSMS Areas and Sub-Areas | Cluster 1 | Cluster 2 | F | p |
---|---|---|---|---|
n = 35 | n = 14 | |||
Food autonomy | 12 | 29 | 74.16 | <0.001 * |
Personal care and hygiene | 25 | 56 | 64.21 | <0.001 * |
Dressing and undressing autonomy | 18 | 52 | 83.52 | <0.001 * |
Sphincter control | 8 | 24 | 91.45 | <0.001 * |
Functional mobility | 59 | 133 | 58.48 | <0.001 * |
Communication and language | 24 | 43 | 21.88 | <0.001 * |
Task solving in social contexts | 9 | 13 | 4.52 | 0.04 * |
Interactive and symbolic play | 11 | 26 | 11.28 | <0.001 * |
Routines in daily life | 3 | 7 | 10.69 | <0.001 * |
Adaptive behaviour | 11 | 13 | 0.40 | 0.53 |
Attention | 4 | 5 | 0.47 | 0.50 * |
Age Group | Cluster | |
---|---|---|
1 n = 35 | 2 n = 14 | |
1 | 9 | 1 |
2 | 14 | 3 |
3 | 6 | 7 |
4 | 6 | 3 |
FSMS Areas and Sub-Areas | Cluster 1 | Cluster 2 | F | p |
---|---|---|---|---|
n = 34 | n = 30 | |||
Food autonomy | 31 | 13 | 109.84 | <0.001 * |
Personal care and hygiene | 63 | 27 | 141.30 | <0.001 * |
Dressing and undressing autonomy | 52 | 19 | 145.20 | <0.001 * |
Sphincter control | 19 | 7 | 33.89 | <0.001 * |
Functional mobility | 140 | 63 | 135.85 | <0.001 * |
Communication and language | 39 | 20 | 58.32 | <0.001 * |
Task solving in social contexts | 10 | 6 | 5.23 | 0.026 * |
Interactive and symbolic play | 27 | 20 | 2.93 | 0.093 |
Routines in daily life | 7 | 4 | 7.03 | 0.010 * |
Adaptive behaviour | 14 | 11 | 1.65 | 0.204 |
Attention | 5 | 5 | 0.008 | 0.929 |
Diagnoses Type | Cluster | |
---|---|---|
1 n = 34 | 2 n = 30 | |
1 | 4 | 10 |
2 | 3 | 6 |
3 | 4 | 8 |
4 | 2 | 6 |
5 | 15 | 0 |
6 | 5 | 0 |
7 | 1 | 0 |
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Sáiz-Manzanares, M.C.; Solórzano Mulas, A.; Escolar-Llamazares, M.C.; Alcantud Marín, F.; Rodríguez-Arribas, S.; Velasco-Saiz, R. Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis. Children 2024, 11, 381. https://doi.org/10.3390/children11040381
Sáiz-Manzanares MC, Solórzano Mulas A, Escolar-Llamazares MC, Alcantud Marín F, Rodríguez-Arribas S, Velasco-Saiz R. Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis. Children. 2024; 11(4):381. https://doi.org/10.3390/children11040381
Chicago/Turabian StyleSáiz-Manzanares, María Consuelo, Almudena Solórzano Mulas, María Camino Escolar-Llamazares, Francisco Alcantud Marín, Sandra Rodríguez-Arribas, and Rut Velasco-Saiz. 2024. "Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis" Children 11, no. 4: 381. https://doi.org/10.3390/children11040381