ABSTRACT

• Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life.
• Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts.
• Mental health is very important at every stage of life, from childhood and adolescence through adulthood.
• This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria.
• A person’s mental well-being is his or her mental condition, as well as an overview of his or her general environment. Brain chemistry abnormalities are the cause of mental illness.
• An individual’s mental health serves as a barometer for properly addressing his or her diseases.
• To predict any health-related irregularities, it is critical to keep track of diverse groups’ mental health profiles.
• The community is made up of working professionals, college students, and high school students.

EXISTING SYSTEM

• There has been many studies and researches where people have been predicting mental health problems like depression and anxiety using the algorithms of machine learning.
• Majorly decision tree, support vector machine, random forest and convolution neural network for the collection and classification of data from blog posts.

PROPOSED SYSTEM

• The initial step is data collection.
• We have tried to collect data from different places. There was no standard dataset available which could match our requirements. Hence, we had to collect all the data ourselves.
• We made a survey form for each disease and distributed, both online and offline for people to fill it.
•  The nature of our questions was objective and situational.
• We also included people who are currently suffering from some kind of mental illness and are seeing doctors for it and taking some kind of medications.
• Once the data collection is done, the user’s response is converted using numeric values of 0 to 3, and in some cases 0 to 4.
• Once we had enough data collected, it was moved to preprocessing and is split into two subsets i.e., training and test data sets.
• It is important to fill out the missing values in the dataset or modify it to increase the quality of the dataset.
• Once the preprocessing of data is completed, it then moved to feature extraction thenceforth prediction of mental illness.

PROJECT VIDEO

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements

• •Hard Disk: Greater than 500 GB
• •RAM: Greater than 4 GB
• •Processor: I3 and Above

Including Packages

=======================

• * Base Paper
• * Complete Source Code
• * Complete Documentation
• * Complete Presentation Slides
• * Flow Diagram
• * Database File
• * Screenshots
• * Execution Procedure
• * Readme File
• * Addons
• * Video Tutorials
• * Supporting Softwares

Specialization =======================

• * 24/7 Support * Ticketing System
• * Voice Conference
• * Video On Demand 
• * Remote Connectivity
• * Code Customization
• * Document Customization 
• * Live Chat Support

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