Mental Health Prediction System Using Machine Learning
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This document discusses a project aimed at detecting depression and mental health disorders using machine learning techniques. It outlines the importance of mental health, the machine learning approach for analyzing data, and the workflow for building a predictive model to determine an individual's mental health status and need for medical support. The document emphasizes the necessity of mental health awareness and the use of machine learning in improving diagnosis and treatment planning.
Mental Health Prediction System Using Machine Learning
- 1. SUBMITTED BY- SOUMA MAITI TRIASHA SAMANATA UNDER GUIDANCE OF DR. DHRUBAJYOTI GHOSH DETECTING DEPRESSION & MENTAL HEALTH DISORDERS USING MACHINE LEARNING
- 2. CONTENTS 1 2 1. INTRODUCTION 2. MACHINE LEARNING IN DETECTING MENTAL HEALTH DISORDERS 3. OBJECTIVE 4. LITERATURE SURVEY 5. SYSTEM ARCHITECTURE 6. DATASET 7. WORKFLOW 8. MACHINE LEARING ALGORITHMS 9. REFERENCE
- 3. INTRODUCTION 1. 1 in 8 people or 970 million peoples worldwide have a mental disorder. 2. 70–90% of people who seek treatment for mental health disorders have a significant reduction in symptoms. 3. Mental disorders can cause significant disturbances in thinking, emotional regulation, or behavior. 4. Mental disorders are caused by a complex interplay of genetic, biological, personality, and environmental factors. 5. Most people who commit suicide have a diagnosable mental disorder, most commonly a depressive disorder or a substance use disorder. 6. Mental illnesses are among the most common health conditions in the India.
- 4. Machine Learning in Detecting Mental Health Disord Machine Learning (ML), as one of the most recent generations of Al technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. Neuroscientists and clinicians around the world are using machine learning to develop treatment plans for patients and to identify some of the key markers for mental health disorders before they may set in. One of the benefits is that machine learning helps clinicians predict who may be at risk of a particular disorder.
- 6. OBJECTIVE The primary goal of this project is to extract patterns from the dataset, and then build a model based on these extracted patterns to know a person is facing depression or any other mental instability or not, if facing whether he/she needs medical counseling or not. The historical data will be used in order to do the analysis and it will also predict the type of mental instability the person facing and whether the person needs medical support or not.
- 7. To diagnose patient’s problem, doctor may ask the patient to fill out a questionnaire. The nature of these questions could be situational and objective, In our project work, We are trying to predict the following problems- 1. Depression 2. Anxiety 3. Insomnia 4. Schizophrenia 5. Phobias
- 8. LITERATURE SURVEY G. Cho, J. Yim, Y. Choi, J. Ko, and S.-H. Lee, “Review of machine learning algorithms for diagnosing mental illness,” Psychiatry Investigation, vol. 16, no. 4, pp. 262–269, 2019. T. I. Mueller, A. C. Leon, M. B. Keller et al., “Recurrence after recovery from major depressive disorder during 15 years of observational follow-up,” American Journal of Psychiatry, vol. 156, no. 7, pp. 1000–1006, 1999 S. Papini, D. Pisner, J. Shumake et al., “Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization,” Journal of Anxiety Disorders, vol. 60, pp. 35–42, 2018. S. Graham, C. Depp, E. E. Lee et al., “Artificial intelligence for mental health and mental illnesses: an overview,” Current Psychiatry Reports, vol. 21, no. 11, 2019.
- 9. REQUIREMENT SPECIFICATION Hardware Used : 1)Computer Windows Software/Code Editor Used: 1)Jupiter Notebook 2)VS Code Language Used: Python 3 Libraries Used: 1)Pandas 2)Numpy 3)Seaborn 4)Matplotlib 5)Sklearn
- 10. MACHINE LEARNING OVERVIEW A machine learning system builds prediction models, learns from previous data, and predicts the output of new data whenever it receives it. The amount of data helps to build a better model that accurately predicts the output, which in turn affects the accuracy of the predicted output. Machine learning uses data to detect various patterns in a given dataset. It can learn from past data and improve automatically. It is a data-driven technology. Machine learning is much similar to data mining as it also deals with the huge amount of the data.
- 13. Machine Learning Models Mental Health Status Type of Problem Whether She/he needs doctor support or not
- 14. Workflow START TEST Values send to backend and pass through machine learning models Model predicts the outcome Result displayed to the user Input Your Answers Answers get saved in form of number
- 15. Data Collection Data Preprocessing Data Transformation Feature engineering Articulating the problem Defining data required Gathering and combining data from different sources Formatting Cleaning Sampling Scaling/normalizing Decomposition Aggregation DATA PREPARATION PROCESS
- 16. DATA COLLECTION Dataset is provided to Machine Learning models on the basis of the facts this version is trained. There are 300 rows and 30 columns in the dataset. Multiple questions are asked to various individuals of all age group and then analyzing every response from the individuals the dataset is made.
- 18. EXPLORATORY DATA ANALYSIS Exploratory Data Analysis (EDA) refers to the method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables.
- 20. DATA PRE-PROCESSING Data pre-processing entails converting raw data into a comprehensible format that a machine learning model can understand.
- 21. DATA CLEANING
- 22. FEAUTURE SELECTION Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve. We do this by including or excluding important features without changing them. It helps in cutting down the noise in our data and reducing the size of our input data. Useful Data Noise Useful Data
- 24. FEATURE SCALING Feature scaling is a data pre-processing technique used to transform the values of features or variables in a dataset to a similar scale. The purpose is to ensure that all features contribute equally to the model and to avoid the domination of features with larger values. Standardization is another scaling method where the values are centred around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero, and the resultant distribution has a unit standard deviation. Here’s the formula for standardization: μ is the mean of the feature values σ is the standard deviation of the feature values. Note that, in this case, the values are not restricted to a particular range.
- 26. MODEL TRAINING A machine learning training model is a process in which a machine learning (ML) algorithm is fed with sufficient training data to learn from. Using k-fold cross validation technique we have evaluate the performance of the model.
- 27. MODEL SELECTION Model selection in machine learning is the process of selecting the best algorithm and model architecture for a specific job or dataset Algorithm Name Accurac y Avg Cross Val Logistic Regression 0.88333 0.90999 Random Forest 0.85 0.92 Gradient Boosting 0.8 0.88333 Algorithm Name Accura cy Avg Cross Val Logistic Regression 0.68333 0.74333 Random Forest 0.71666 0.79 Decision Tree 0.6666 0.71 KNN 0.5333 0.556666 Algorithm Name Accura cy Avg Cross Val Logistic Regression 0.86666 0.8766 Random Forest 0.83666 0.86333 Decision Tree 0.7833 0.80333 Gradient Boosting 0.81666 0.86333 Mental Status Type of problem Doctor Need
- 28. Random Forest Algorithm Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning. Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. The greater number of trees in the forest leads to higher accuracy and prevents the problem of over fitting. The below diagram explains the working of the Random Forest algorithm:
- 30. HYPERPARAMETER TUNING Hyper parameters tuning is the process of determining the right combination of hyper parameters that maximizes the model performance. It works by running multiple trials in a single training process. Each trial is a complete execution of your training application with values for your chosen hyper parameters, set within the limits you specify. This process once finished will give you the set of hyper parameter values that are best suited for the model to give optimal results. RANDOM SEARCH CV In Random Search, in a given grid, the list of hyper parameters are trained and test our model on a random combination of given hyper parameters.
- 31. MODEL DEPLOYMENT Finally, we are done so far. The last step is to deploy our model in production map. So we need to export our model and bind with web application API. Using pickle we can export our model and store in to CLF.pkl file, so we can ealy access this file and calculate customize prediction using Web App API.
- 32. USER INTERFACE The user interface of the app is made on Streamlit App. Streamlit is a free and open-source framework to rapidly build and share beautiful machine learning and data science web apps. It is a Python-based library specifically designed for machine learning engineers.
- 36. FINAL RESULTS
- 37. CONCLUTION Based on different parameter given to the model, model predicts the mental health state of the person and predicts the type of the problem if the person have and also predicts whether the person need medical support like counseling or therapy. In our project using machine learning algorithms we tried to correctly predict it. The conclusion about mental health is that it’s a crucial aspect of overall well-being and requires attention , understanding and support from both individuals and society as whole. It’s essential to prioritize mental health awareness , destigmatize seeking help and promote access to quality mental health care service.
- 38. REFERENCES www.greekforgreeks.com www.scholarhat.com https://www.javatpoint.com/machine-learning
- 39. THANK YOU