Using machine learning to comprehend the mental health of students

Introduction

The issues of college include things like school stress, problems with friends, and a change in lifestyle that may affect their mental health. Researching teachers have found out that those setting rules are starting to see more of these cases nowadays and concluded that we urgently take measures put up mechanisms to control the situation when it is had (Williams & Martin 2). Nevertheless, students have a hard time asking for help because the society expects them to be self-reliant or else they are weak if they visit psychologists or counsellors at school for instance. This lack of openness leads to minimal conversation among individuals who interact with peers hence many psychological issues remain unresolved.

Researchers have begun employing a sort of self-learning technology to facilitate the monitoring and management of mental health among students. It is quite difficult to notice such patterns as excellent results, being a student's friendliness with mates, as well as behavior without understanding accurately person’s mental state when dealing directly with large data sets such as grades, relations in class or student behavior itself. As a result, it has made it easy for individual student beneficiaries to be recipients of personalized assistance geared towards reducing academic stress levels.

This research brings new knowledge to the ever-growing body of research in this area, including the promotion of artificial intelligence in mental health studies, through rigorous experiments. This investigation can support individual-based interventions and support systems that can improve overall life satisfaction and academic achievements for students. The article also provides an overview of some of the challenges and dilemmas that come with the deployment of machine learning models into the privacy-sensitive areas, such as mental health care, and speaks to responsible application of technologies to protect students' rights to privacy and self-governance.

Researchers have begun employing a sort of self-learning technology to facilitate the monitoring and management of mental health among students. It is quite difficult to notice such patterns as excellent results, being a student's friendliness with mates, as well as behavior without understanding accurately person’s mental state when dealing directly with large data sets such as grades, relations in class or student behavior itself. As a result, it has made it easy for individual student beneficiaries to be recipients of personalized assistance geared towards reducing academic stress levels.

Literature Review & Methodology

Methodology

This part gives a full summary of the machine learning techniques used in this article to solve the problem of movie classification. A distinct algorithm for supervised machine learning were concentrated on by the researcher in this study including Random Forest, AdaBoost, Support Vector Machines, and K nearest neighbour. With a metropolis within 20 miles, universities are situated in suburban and rural (college town) environments. Because of this, these four datasets are distinct and suitable for used as benchmarks for different machine learning models[9]. The steps involved in the methodology followed are feature extraction, pre-processing and evaluation of different matrices for each model used in this research.

Dataset collection

At this stage, data was visually represented and patterns were discovered. This study employed the dataset from the Kaggle repository called "student stress factor." A full description of the characteristics is located in the table below that goes with it.

Preprocessing

There are 1100 rows and 21 columns in the original dataset. The table below represents various features in the dataset (Table 1). All the data is used to assess the effectiveness of many supervised machine learning models. The dataset is pre-processed and split into two files, which are tested and trained with a ratio of 90:10

Results

The analysis of data revealed positive results, as the average accuracy was at 96.7% for all classification models. Different algorithms were used to assess each model’s performance, such as Support Vector Machines (SVM), Random Forest, Ada Boost, k-Nearest Neighbors (kNN), Neural Networks and Naïve Bayes.

Support Vector Machines showed strong performance with 89.3% accuracy. The accuracy of Random Forest was also found to be close to that with a percentage of around 99.4%. On its side, Ada Boost achieved an accurate prediction with no mistake since it scored 100%. This displays the effectiveness of k-Nearest Neighbors in classifying the dataset since they had an accuracy value of 99.7%.

The Neural Network algorithm finally reached a milestone by achieving an accuracy of 95.1%.

From these results, the dataset was suitable for classification task while using different algorithms which gave a good idea about what underlies this dataset. High accuracies from various models indicate that the dataset was robust while classifying using various techniques were effective.

Conclusion

According to our research, machine learning is crucial for university students' mental health. Different algorithms such as SVM, KNN, AdaBoost, Neural Network, and Random Forest have been applied to predict future outcomes linked to mental wellbeing based on school performance records on social networks. we found out promising results in our study, which achieved high accuracy in different performance measures. These results show machines efficiency in showing secret patterns as well as predicting personal mental health changes among students that help design individualized remedies.