Mental health model to assess psychiatric fitness in women by speech analysis using machine learning.

Women psychiatric health is one of the most neglected factors. Women mental health is very important as mentally healthy woman is the backbone of entire family and society. Even though many papers are published related to mental illness detection and treatment using various methods through survey, questionnaires, public health databases, clinical records, social media usage etc., by applying different Machine Learning (ML) algorithms for prediction, still there is huge room left for identifying and analyzing the mental illness through different means and applying ML for prediction, detection and assessment. The main aim of this research work is to demonstrate that speech is the easiest way to recognize symptoms of mental illness like depression, stress, anxiety, trauma etc. The speech signal carries hidden attributes like intensity, pauses, speech rate which reveal lot of information about the psychological fitness of a woman. The model is deployed on all the kernels of SVM to study and analyze the prediction accuracy of classification using three classification labels. The result obtained is more realistic in assessing the psychiatric fitness with overall accuracy of 90.78 percent. [ABSTRACT FROM AUTHOR]