Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Area of Science:

• Computational biology and bioinformatics
• Machine learning applications in healthcare
• Predictive modeling for metabolic diseases

Background:

• Metabolic syndrome (MetS) is a complex condition characterized by a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity.
• MetS is associated with increased risks of diabetes, heart disease, cancer, and chronic kidney disease, leading to substantial healthcare costs.
• Accurate and timely prediction of MetS is crucial for early intervention, lifestyle modification, and improving patient quality of life.

Purpose of the Study:

• To evaluate the performance of various machine learning algorithms for predicting metabolic syndrome.
• To investigate the effectiveness of metaheuristics for feature selection in MetS prediction models.
• To assess the impact of data augmentation on the accuracy of MetS predictive models, aiming to reduce diagnostic costs.

Main Methods:

• Employed ten distinct machine learning algorithms for classification tasks.
• Utilized various metaheuristic approaches for optimizing feature selection from the dataset.
• Applied data augmentation techniques to enhance the training data and improve model generalization.

Main Results:

• Feature selection using metaheuristics, followed by data augmentation, significantly improved the predictive performance of machine learning classifiers.
• The combination of optimized feature selection and data augmentation demonstrated superior accuracy in identifying individuals at high risk of MetS.
• The study confirmed that data augmentation is a key factor in boosting classifier performance after feature selection.

Conclusions:

• Machine learning models, particularly when enhanced with optimized feature selection and data augmentation, offer a cost-effective approach for early metabolic syndrome prediction.
• Early identification of MetS through advanced computational methods can facilitate timely interventions and potentially mitigate long-term health complications.
• The findings highlight the potential of data augmentation strategies to improve the accuracy and reliability of predictive diagnostic tools in metabolic health.