A Machine Learning Approach to Detect Self-Care Problems of Children with Physical and Motor Disability

Inability to look after and take care of own self in a healthy way is referred to as self-care problems. Physical disabilities are limitations in physical capacity, mobility of a person. Motor disability is defined as the loss of function of any body part. Self-care problems detection is an important sector of the exceptional children health care system because depending on the type of self-care problems, the treatment approach is chosen. Expensive involvement of professional therapists and a long time is required to detect self-care problems. To predict the self-care problems of children with physical and motor disability in advance, an expert system is proposed using machine learning. To do the task, the only available self-care activities dataset SCADI (Self-Care Activities Dataset based on ICF-CY) is used. Principal Component Analysis (PCA) is used to reduce the feature vector size. Different machine learning classification algorithms are used to find out the prediction accuracy. Prediction performances on different partitions of the dataset are analyzed. Finally, a system with K Nearest Neighbor (KNN) is proposed to predict self-care problems.