DETECTION OF DEPRESSION USING ACOUSTIC AND TREATMENT RESPONSE PREDICTION USING MACHINE LEARNING

Abstract

The easiest way to beat the depression blues is to recognise the symptoms and seek therapy right away. Depression diagnosis is based on the patient's openness and level of assistance. There are many diagnostic and assessment tools for depression, including as interview-style testing, objective screening methods, and automatic speech, video, and text identification. The entire procedure takes time, and remote diagnosis is also not an option. The automatic diagnosis from speech, video, and text requires much less time and no highly trained medical professional. If depression is found, the patient can be directed to a skilled and knowledgeable physician. In our study, the website established aids in rapid self-diagnosis of depression with the use of machine learning algorithms. Here we take the audio of the patient to detect whether the person is depressed or not and then we implement in naïve bayes and get the decision as whether that person is depressed or in normal state.