Implementation of Multivariate Logistic Regression Model for Cerebral Palsy Identification using Prenatal, Perinatal Risk Factors

Cerebral Palsy (CP), a static, neuro and motor disorder caused by brain injury in the time period of prenatal, perinatal and postnatal, is the major developmental disability affecting children's function. Children with CP in children cannot be curable but quality of life can be improve with the help of treatment such as surgery and therapy. Early identification is important to the CP children for starting the treatment. There are numerous Machine Learning (ML) algorithms used in health care for prediction and classification. One of the ML algorithms called Logistic Regression which is used for binary classification using univariate and multivariate. This study, is of interest to enable early identification of CP using prenatal and perinatal risk factors with help of Multivariate Logistic Regression.