- 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.
- 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.
- 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.
- Front End – Anaconda IDE
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