Hybrid Classifier for Optimizing Mental Health Prediction: Feature Engineering and Fusion Technique

A major worldwide health concern is mental health issues, which highlights the importance of early identification and intervention. In this paper, the effectiveness of two new hybrid classifiers is examined and compared to traditional machine learning techniques. Our study presents a novel hybrid classifier framework that combines Decision Trees with k-Nearest Neighbors (Hybrid_1) and Random Forest with Neural Networks (Hybrid_2). We do a detailed study with an emphasis on customized feature engineering techniques for mental health evaluation utilizing this novel fusion technique. The results of the experiments conducted on the Mental_health.csv dataset show how well the hybrid classifiers work; accuracy rates of 86.69% and 93.54%, respectively, for (DT + kNN) and (RF + NN) is attained. The aforementioned results highlight the potential of hybrid classifiers to improve mental health prediction and highlight the importance of feature engineering in optimizing predictive models. By combining Decision Trees with k-Nearest Neighbors and Random Forests with Neural Networks, respectively, our hybrid classifiers, Hybrid_1 and Hybrid_2, surpass current techniques and mark a breakthrough in the prediction of mental health. Our hybrids take advantage of the complimentary capabilities of various algorithms, in contrast to traditional techniques that could have trouble with complex feature connections or be less flexible when working with different datasets. In addition to showcasing the potential of hybrid classifiers in mental health assessment, our results offer insightful information on feature selection and model explainability, furthering our understanding of this important area.