## 1 Introduction
### Related Works  
In this work, the ICF-CY self-care dataset was used to develop ML models for the detection of children with disabilities. [PERSON] et al. [15] employed SCADI's original datasetfor self-care research and demonstrated the application of SCADI in expert systems. The author employed ANN for classification and a C4.5 decision tree (DT) to extract self-care problems; both models performed 10-fold cross-validation during training and testing. The experiment's findings demonstrated that ANN was highly accurate in anticipating self-care issues, with a prediction rate of 83%. In yet another study [16], several ML classification algorithms were employed to predict accuracy based on a 5-fold CV using principal component analysis (PCA) to minimize the dimensionality of features. With an accuracy of about 84%, KNN was chosen as the best classification approach for predicting self-care issues. [PERSON] et al. [17] proposed a new method for feature selection based on information gain regression curve feature selection (IGTCFS) that uses a SCADI dataset to discover the most important properties for each class. In eight different datasets, the IGTCFS approach was tested against five different feature selection methods. With 10-fold cross-validation to assess Naive Bays (NB) after extracting the most relevant characteristics from the SCADI dataset using IGTCFS; the greatest accuracy obtained is 78%. In another work, [PERSON] et al. [18] proposed a novel ML model on the SCADI dataset in which the dataset is turned into a binary class to identify whether children were identified with negative or positive self-care difficulties. Fuzzy neural networks were used to classify whether or not children had self-care problems; the model achieved a test accuracy of 85%. [PERSON] [19] used deep neural networks and extreme learning machine models to classify two datasets: [PERSON]'s disease and self-care problems. The split-train tool and test set were used to classify the models with 60% used for training and 40% for testing. Both models show the best accuracy of 97% and 88%, respectively. Moreover, [PERSON] [20] also provides a hybrid auto-coding framework for classifying the self-care problem in multi-class and binary-class datasets. The system combines a method of automated encoding with deep neural networks. The classification model was tested using 10-fold cross-validation, and its accuracy was 84% for multi-class data and 91% for binary-class data. [PERSON] et al. [21] suggested using the standard 0.05 threshold technique together with a correlation-based feature selection (CBF). Random forest was used to classify the ten original datasets that were processed using FFT and IFFT to evaluate the threshold value determination. The study demonstrated that using a standard deviation threshold when processing the initial dataset improved feature selection accuracy and sped up the random forest classification process on average. [PERSON] [22] proposes a better learning model that makes use of a variety of machine learning strategies to predict the patients' severity. An adaptive boost method combined with a decision tree estimator and a new parameter tuning procedure are used in the suggested model.