Enhancing Bipolar Disorder Detection using Heterogeneous Ensemble Machine Learning Techniques

Abstract

This paper introduces a novel Heterogeneous Ensemble Machine Learning (HEML) approach designed to detect bipolar disorder, a significant healthcare challenge that demands precise and prompt diagnosis for effective treatment. The HEML method integrates multiple machines learning models, incorporating various physiological, behavioral, and contextual data from patients. By using a comprehensive feature selection technique, relevant features are extracted
from each data source and utilized to train individual classifiers for detecting mental disorders. The classifiers include Adaboost, Decision Tree, K-nearest neighbors, Multilayer Perceptron, Random Forest, Relevance Vector Machine, and XGB, with Logistic Regression serving as the meta-model. This ensemble of classifiers enhances overall performance by capturing a wider range of characteristics related to mental disorders. The research evaluates the HEML method across
three bipolar disorder datasets: Dataset1 (a multimodal dataset), Dataset2 (a sensor-based dataset), and Dataset3 (a real-time dataset). The HEML approach surpasses traditional methods, achieving superior accuracy rates of 95.21% with Dataset 1, 99.28% with Dataset 2, and 99% with Dataset 3. It outperforms individual models in detecting bipolar disorder, delivering the best Precision, Recall, F1 score, and Kappa Score. This comparative analysis advances the field of mental health diagnosis by leveraging the strengths of ensemble machine learning to improve accuracy and
reliability in detection methods.