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Identification of self-care problem in children using machine learning
1Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
Insights
Machine learning models can now identify childhood self-care problems with 99% accuracy. This approach simplifies diagnosis, addressing challenges faced by medical professionals and occupational therapists.
Area of Science:
- Pediatric Health
- Artificial Intelligence
- Machine Learning in Healthcare
Background:
- Identifying self-care problems in children is complex and time-consuming for medical professionals.
- A global shortage of occupational therapists exacerbates the challenge of diagnosing these issues.
- Machine learning offers a potential solution to simplify and expedite the identification process.
Purpose of the Study:
- To employ machine learning models for identifying self-care problems in children.
- To evaluate the effectiveness of different classification algorithms on the SCADI dataset.
- To improve diagnostic accuracy and efficiency in pediatric self-care assessments.
Main Methods:
- Utilized the SCADI dataset for training and testing machine learning models.
- Addressed high dimensionality through data reduction techniques.
- Applied SMOTE (Synthetic Minority Over-sampling Technique) to balance the imbalanced dataset.
- Compared Naïve Bayes, J48, and Random Forest classification algorithms.
Main Results:
- The Random Forest classifier achieved the highest performance on the SMOTE-balanced data.
- Achieved a balanced accuracy of 99% in identifying self-care problems.
- The developed machine learning model surpassed the performance of existing expert systems.
Conclusions:
- Machine learning, particularly Random Forest with SMOTE balancing, is highly effective for identifying childhood self-care problems.
- This approach offers a more accurate and efficient alternative to traditional diagnostic methods.
- The study demonstrates the potential of AI to support healthcare professionals in pediatric assessments.
Abstract:
Identification of self-care problems in children is a challenging task for medical professionals owing to its complexity and time consumption. Furthermore, the shortage of occupational therapists worldwide makes the task more challenging. Machine learning methods have come to the aid of reducing the complexity associated with problems in diverse fields. This paper employs machine learning based models to identify whether a child suffers from self-care problems using SCADI dataset. The dataset exhibited high dimensionality and imbalance. Initially, the dataset was converted into lower dimensionality. Imbalanced dataset is likely to affect the performance of machine learning models. To address this issue, SMOTE oversampling method was used to reduce the wide variations in the class distribution. The classification methods used were Naïve bayes, J48 and random forest. Random forest classifier which was operated on SMOTE balanced data obtained the best classification performance with balanced accuracy of 99%. The classification model outperformed the existing expert systems.
