Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

“…Table 11 summarizes the comparison study of the proposed model with previous related works. In general, the proposed model has outperformed all previous study results applied on the same dataset such as ANN [4], KNN [5], NB [6], SMOTE + XGBoost [7], FNN [8], DNN [9], and hybrid autoencoder [10]. The best accuracy for multi-class classification problem on the SCADI dataset still goes for DNN [9]; however, it should be noted that they used hold-out validation method (60%/40% for training and testing) which is less reliable and increase the possibility of over-fitting and over-optimism, as compared with 10-fold cross-validation [47].…”

“…The result showed that the maximum accuracy was achieved by DNN and ELM at 97.45% and 88.88%, respectively. Furthermore, a hybrid autoencoder for classifying the self-care problem based on the combination of autoencoders and deep neural networks (DNN) was proposed by Putatunda (2020) [10]. The proposed model was tested using the 10-fold CV, achieving average accuracy by up to 84.29% and 91.43% for multi-and binary-class datasets, respectively.…”

“…In our study, we considered the original multi-target/category output from the SCADI as the dataset I. In addition, we followed previous studies [8,10] in converting the target class output from a multi-class problem to a binary-class problem and we considered this as dataset II. The final target class output for dataset II is set to 0 if self-care problem is not present in the subject and to 1 for all the subjects who have been categorized as having self-care problems.…”

“…This framework has been used as a standardized guideline to classify self-care problems [3]. Several previous studies have utilized machine learning algorithms, such as the artificial neural network (ANN) [4], k-nearest neighbor (KNN) [5], naïve Bayes (NB) [6], extreme gradient boosting (XGBoost) [7], fuzzy neural networks (FNN) [8], deep neural networks (DNN) [9], and hybrid autoencoder [10] to help occupational therapists improve the classification accuracy and reducing the time as well as the cost of self-care classification [11].…”

Self-Care Problems Classification of Children with Physical and Motor Disability by Deep Neural Networks

“…Öznitelik seçimi yaptığında ise bu oran %84.75'e yükselmiştir. Putatunda [13] [17]. Derin öğrenmeyi kullanarak derin mimarilere sahip derin sinir ağları (DSA) kurulabilir.…”