Depression and Anxiety prediction through Machine Learning
Machine learning algorithms employ a variety of statistical, probabilistic and optimization methods to learn from past experience and detect useful patterns from large, unstructured and complex datasets. These algorithms have a wide range of applications, including automated text categorization, net
2025-06-28 16:31:07 - Adil Khan
Depression and Anxiety prediction through Machine Learning
Project Area of Specialization Artificial IntelligenceProject SummaryMachine learning algorithms employ a variety of statistical, probabilistic and optimization methods to learn from past experience and detect useful patterns from large, unstructured and complex datasets. These algorithms have a wide range of applications, including automated text categorization, network intrusion detection, junk e-mail filtering, detection of credit card fraud, customer purchase behavior detection, optimizing manufacturing process and disease modelling. Most of these applications have been implemented using supervised variants of the machine learning algorithms rather than unsupervised ones. In the supervised variant, a prediction model is developed by learning a dataset where the label is known and accordingly the outcome of unlabeled examples can be predicted [01].
The proposed study attempts to provide an opportunity to know the depression of an individual that whether he/she is depressed or not depressed using supervised machine learning techniques. Besides, this proposed study will not only save the people from the depression mode but also prevent from negative thoughts which may cause any more harmful act. It is being to be known that how depression is a big problem nowadays, so it is being to find out a way through supervised machine learning algorithms on the dataset to predict the depression of any individual. In this study, the dataset related predicting depression would be collected. Secondly, the performances of the different classifiers would be compared.
Project ObjectivesThe objective of the proposed study would be applied in the following manner;
? In depth analysis of the problem and causes related to depression.
? To collect data from social network.
? To select a suitable algorithms for comparison and interpretation of results.
? To train and test classifiers using collected data
Phase 1: Data Collection
During this phase dataset of tweets will be collected from online sources; primary and secondary. And the dataset would be consisted of two class labels i.e., positive and negative or 0 and 1.
Phase 2: Preprocessing steps
During this phase, several preprocessing steps would be applied on tweets. First, tokenization would break paragraphs into sentences and sentences into words. Second, Stop Word Filtering would remove unnecessary words from the dataset. Finally, Stemming would look for root words.
Phase 3: Classification using Machine Learning Algorithms
In this stage, first of all 70% of the dataset will be trained on different algorithms and 30% of the dataset will be used for testing purpose.
It has been observed that depression has emerged as a very serious health issue for the people of every age, gender, and race. It would be a dire need of to find a way to predict the depression because this remedy will not only save the people from the malady of psychological disorder but also prevent them from negative thoughts which might affect other individuals in society. The proposed solution finds the way through the depression would be predicted and many of the problems can be avoided.
Technical Details of Final DeliverableIn depth analysis of the problem and causes related to depression. (3 months)
? To collect data from social network. (3 months)
? To select a suitable algorithms for comparison and interpretation of results. (3 months)
? To train and test classifiers using collected data (3 months)
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 80000 | |||
| External Hard disk 4TB | Equipment | 1 | 22000 | 22000 |
| Cisco Firewall Server | Equipment | 1 | 48000 | 48000 |
| paper publication and thesis cost | Miscellaneous | 1 | 10000 | 10000 |