Child Abuse Mental Symptom Prediction Model using Machine Learning Techniques

The most frequent psychiatric disorders are depression and post-traumatic stress disorder. Machine learning algorithms are effective tools for predicting psychiatric diseases. A SVM was used to create a prediction model. As a side effect, proper precaution to protect every child from any form of violence. In India, Child Violence had recently been published Major problem. The extent to which society is not widely known, most of the time disguised, many more victims does not mention in a public health concern is child abuse and neglect. This Project is mainly Focused To rehabilitate the Child, Abuse show various methods is bodily, mind, and several abuse, as well as domestic violence. A child who is subjected to any of the aforementioned sorts of abuse is at danger of being isolated, developing anxiety, depression, and mental trauma, as well as having difficulty learning and building social relationships. Anxiety and de pression in children are frequently mis diagnosed. The proposed research study has used the LS TM algorithm to design and develop a Child Abuse Depression Detection and Recommendation Model to recognize the signals of anxiety and depression especially in young children. On Child Abuse Depression Symptom instances in India, the proposed Deep-LSTM ensemble model is assessed using several forms.