A Hybrid Technique Combining Robust and DNN for Thyroid Disease Prediction on Imbalanced Dataset

Thyroid disease poses an important public health risk, reducing the quality of life and increasing healthcare expenditures. In recent years, Artificial Intelligence (AI) algorithms have become essential tools for diagnosing and predicting thyroid disease. Although various AI-based algorithms have been applied to this task, their performance is often hindered by imbalanced data. This research proposes a hybrid approach that combines robust techniques and Deep Neural Networks (DNN) to address this challenge in thyroid disease prediction. The present paper employs the publicly available thyroid disease dataset, obtained from the University of California, Irvine machine learning repository. To manage the data imbalance, Random Under (RU) and Random Over (RO) sampling techniques are applied. A comparative analysis reveals that the hybrid approach, combining robust and DNN with RO sampling, significantly outperforms the RU sampling method on the imbalanced dataset. To assess the effectiveness of the proposed technique, the variant criteria are employed such as accuracy, F۱-score, recall, precision, Area Under Curve (AUC), confusion matrix, and Receiver Operating Characteristic (ROC) curve. The hybrid approach achieves superior results, with a testing accuracy of ۹۸.۷۱%, recall of ۱۰۰%, precision of ۹۷.۴۵%, and an F۱-score of ۹۸.۷۱%. These findings demonstrate the robustness and effectiveness of the approach, underscoring its potential for real-time disease prediction.

کلیدواژه ها:

Deep Neural Network (DNN) ، Robust ، Prediction ، Imbalanced dataset ، Thyroid Disease ، Accuracy