MENTAL HEALTH PREDICTION USING MACHINE LEARNING

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Mental health issues can be predicted by using machine learning. It is a critical task which involves learning the process with sensitive data about specific person. The initial aim is to determine the prospect of someone having or improving a mental health problem based on different criteria.phdtopic.com offers various solutions to PhD and MS scholars for their machine learning concepts. We guide you to bring out the correct topics and title ideas, incase if you are struggling with selection of topics. This article leads us to create a project related to mental health predictions,

  1. Problem Definition:

If the individual person at risk of improving or presently experiencing a mental health issue, we must predict the issue based on provided characteristics.

  1. Data collection:

Suppose our problem depends on the enumeration and the scope, it can be solved with required dataset attributes like:

  • Demographics such as age, gender, race, etc.
  • The family history of mental health conditions.
  • The medical history of specific person.
  • Use of substance like alcohol and drugs.
  • The cycle of sleep patterns.
  • The exposure of stress or trauma.
  • The data survey which responses to standardized mental health
  • The other relevant characteristic depends on experts in this field.
  1. Data Pre-processing:
  • Handle Missing Values: To handle the missing values, we must utilize the given techniques especially in sensitive nature of data.
  • Categorical Encoding: We have to convert the variables to numerical format.
  • Normalization/Standardization: Every variable must be on a parallel scale.
  • Data Splitting: Split the dataset into training, validation and test sets.
  1. Exploratory Data Analysis (EDA):

EDA creates the distribution of target variable and features. It helps to examine our relationship and correlation in-between the attributes and results of mental health.

  1. Feature Selection/Engineering:

To select the most appropriate features, the domain knowledge should be used. If it is required, we use methods like Principal Component Analysis (PCA) for dimensionality reduction.

  1. Model selection:

We can explore with various machine learning algorithms like,

  • Logistic Regression
  • Decision Trees and Random Forest
  • Gradient Boosted Trees such as XGBoost, LightGBM.
  • Support Vector Machines (SVM)
  • Neural Networks
  1. Model Training:

By using the training data, we can able to train the selected models.

  1. Evaluation:

The suitable principles should be used by us on the validation and test datasets like,

  • Accuracy
  • Precision, Recall and F1-score
  • ROC-AUC
  1. Optimization:
  • Hyper parameter Tuning: The model parameters are adjusted for better performance.
  • Ensembling: For better veracity, we can integrate predictions from various models.
  1. Deployment:

We utilize this method in some of the applications like clinical settings, mobile health applications, or as a section of broader health monitoring system.

  1. Feedback and Iteration:

Update the model regularly with new data and feedback from the experts in this field. The model’s real-world performance must be watched and have to clarify by us.

  1. Ethical and Privacy considerations:
  • Data Sensitivity: When we are supposed to handle the sensitive mental health data, it needs most extreme care and must be loyal to privacy regulations.
  • Transparency & Accountability: The users have to know about the process of model which can make predictions and provides structure for human analysis.
  • Bias & Fairness: The model can be examined by us and make sure that it does not treat unfairly against any group.

Tools and Libraries:

  • Data Handling & EDA: Some of the tools used in this area is pandas, NumPy, Matplotlib, Seaborn.
  • Machine Learning: Here we use some tools like, scikit-learn, Tensorflow, Keras, and XGBoost.

Final Thoughts:

  •             The prediction of mental health conditions using machine learning is a sensitive and difficult task to attain. We handle carefully the research work and approach it with extensive care. The model’s predictions should does not accidentally hurt individuals or generate biases. We also make sure that our model’s predictions are similar with the expert analysis are used wisely. We ensure that your research proposals reflect about the understanding of the subject, as well as its potential so that you can performing well on the research work. We also focus in helping out scholars to for their synopsis.

MENTAL HEALTH PREDICTION USING MACHINE LEARNING PROJECTS

 

  1. Comparative Analysis of Machine Learning Techniques for Mental Health Prediction

Keywords:

Logistic regression, K-Nearest Neighbors, Decision tree, Mental Health, Random forest

We utilize the ML methods to predict the mental health disorder by utilizing a dataset contains self-reported information. Our paper utilized four frequently used ML methods like KNN classifier, LR, RF and DT to compare their performance. The aim of our study is to contrast the performance of ML methods on self-reported mental health dataset and find the most applicable method to predict mental health.

  1. Application of Machine Learning to Predict Mental Health Disorders and Interpret Feature Importance

Keywords:

Machine learning, data mining, artificial intelligence, feature importance, model selection, interpretative machine learning, prediction, visualisation

Our study offers an in-depth analysis of mental health survey and observes how we applied it to AI/ML domain of mental health research and how ML can utilize this domain for fitting and prediction. We can access and visualize the presence and absence of mental health disorder on other characters. We find that the Cross Gradient Booster (Random Forest) method gives the best performance among all other ML methods and we also utilize Grid Search method to confirm the final model.

Mental Health Prediction Using Machine Learning Ideas

  1. Non-Invasive Mental Health Prediction using Machine Learning: An Exploration of Algorithms and Accuracy

Keywords:

outcomes, study, datasets, mental health patient questionnaires, MRI scans, AdaBoost

Our study examines ML methods to predict the mental health result. We collected two datasets namely: mental health patient questionaries and the other contains Alzheimer’s patients MRI scans. First, we have to preprocess the datasets by utilizing the approach like stop word removal and lemmatization, and the data we processed can be encoded to improve prediction accuracy. To identify the high accuracy model, we utilize the ML methods like LR, DT, KNN, AdaBoost and RF. The result shows our ML methods give best accuracy.

  1. Machine Learning based approaches for Identification and Prediction of diverse Mental Health Conditions

Keywords:

Depression, Deep Learning, SMOTE, mRMR, NLP, TF-IDF, Elastic Net

We discover different sources of data collection, automated methods and ML-based depression detection methods of various modules like classification, regression methods, DL and ensemble methods are the methods we utilized to analyse varied metal health condition. We also make use of Synthetic Minority Oversampling Technique (SMOTE) to decrease the class imbalance of training data to attain high accuracy.

  1. Mental Health Prediction Among Students Using Machine Learning Techniques

Keywords:

Mental health problems, Feature selection algorithm, KNN classifiers, Neural network

Recently ML methods are well suitable for the analysis medical data and diagnosis of mental health data. We apply some ML methods like LR, DT, RF, KNN classifiers and NN and we have to compare their accuracy on various metrics. We have to gather the datasets for training and testing the performance of the method. By applying the feature selection method we have to enhance the accuracy of our suggested method. Our NN is the most suitable method for this type of prediction.

  1. A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms

Keywords:

Support Vector Machine, Multilayer Perceptron

The goal of our paper is to classify anxiety issue by using a pre-clinical mental health dataset collection. The initial part of our paper aims to develop and execute a prediction model based on classification into five pre-clinical anxiety stages namely: minimal anxiety, mild anxiety, moderate anxiety, severe anxiety and very severe anxiety. The second part offers recommendation for who suffer anxiety disorder. We used feature selection and prediction to predict correct stage. We used three methods: SVM, MLP and RF to predict anxiety stage.

  1. Ensemble Learning Approach for Predicting the Necessity of Mental Health Treatments of Employees

Keywords:

Employees, Ensemble Learning

The goal of our study is to construct the ML method to predict the mental health condition prediction and the need of treatment of the employees. We have to collect the datasets for preprocessing and analyse them by utilizing the methods like NB, DT (J48), SVM, MLP, RF and Ensemble Learning. We utilized 10-fold cross validation to validate the outcome. Our ensemble learning is the best method for predict the level of need of treatments for mental health of employees.

  1. Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students

Keywords: 

Mental well-being; algorithms; university students; Asian population; health behaviors

AI and ML methods can be connected for prediction, early detection and prognostication of negative psychological well-being states. Our work reports on the performance of utilizing different ML method like Generalized Linear model, KNN, NB, NN, RF, Recursive partitioning, bagging and boosting. Our RF and Adaptive Boosting methods give the best accuracy for negative mental well-being.

  1. Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms

Keywords:

XGBoost

Our study uses ML methods to find the suitable mental health disorder in an individual to attain the goal. We also employed supervised ML methods to predict mental health status like KNN and XGBoost to evaluate their performance by using the metrics. When comparing these methods XGBoost becomes an effective predictive model. We create a questionnaire for mental health issue by using web-based method.

  1. Single classifier vs. ensemble machine learning approaches for mental health prediction

Keywords:

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The goal of our paper is to estimate some popular ML methods in classifying and predicting mental health issue based on dataset both from single classifier method as well as ensemble ML method. We use the ML methods like LR, Gradient Boosting, NN, KNN and SVM as well as Ensemble methods using this method. Comparison made against recent ML methods like Extreme Gradient Boosting and Deep NN. Our Gradient Boosting method gives the high classification accuracy.