An ensemble machine learning-based approach to predict thyroid disease using hybrid feature selection

Abstract

Thyroid issues are becoming more and more common, and early detection is critical for therapy that reduces mortality and complications. Because of these factors, detecting thyroid problems has become more crucial in the medical field. Estimating the course of a disease accurately and understanding how clinical features interact are critical for medical diagnosis and treatment. All of these limits are overcome in our study by using a standard machine-learning model with proper clinical feature analysis and an ensemble-learning technique. Predicting sickness progression and the interdependence of clinical features or aspects are critical in medical diagnosis and therapy. However, machine learning has enabled us to detect the risk factors for this sickness. To select the best thyroid prediction outcome, we used five machine learning models in addition to the Ensemble ML classifier (hard voting). Class balancing approaches greatly increase classification performance. It has been demonstrated that using random oversampling improves classification results dramatically. Based on the experimental data, our suggested model outperforms existing methods by a wide margin. Using the XGBoost and SelectKBest feature selection strategies, the Ensemble ML classifier achieves the best results on hard voting on RF and DT, with 100 % sensitivity and 99.71 % accuracy. When features are decreased and the issue of high-class imbalance is addressed, the ensemble ML classifier (hard voting) performs better in dealing with classification challenges.