EARLY DETECTION OF THYROID CANCER USING TIME SERIES ANALYSIS AND QUADRATIC DISCRIMINANT ANALYSIS

Thyroid cancer, a prevalent and potentially life-threatening disease, demands early detection for effective treatment. This study proposes a novel approach for the early detection of thyroid cancer by employing Time Series Analysis (TSA) and Quadratic Discriminant Analysis (QDA). The integration of these techniques aims to enhance diagnostic accuracy and reliability, providing a valuable tool for clinicians to detect the disease in its early stages. In our approach, TSA was used to extract meaningful patterns and trends from temporal data, offering valuable insights into the evolving health status of the thyroid. Subsequently, QDA was applied to build a robust classification model, using the identified time series features to distinguish between cancerous and non-cancerous cases. The application of TSA and QDA yielded promising results, demonstrating high sensitivity and specificity, and outperforming traditional diagnostic methods. Our model achieved an accuracy of 97.72%, precision of 90.91%, sensitivity of 94.01%, and specificity of 98.2%. The incorporation of temporal dynamics through TSA provided a nuanced understanding of the evolving pathology, contributing to the enhanced accuracy of the diagnostic model. In conclusion, this study introduces a novel methodology for early thyroid cancer detection, combining the strengths of TSA and QDA. The results highlight the effectiveness of this integrated approach in improving diagnostic accuracy, particularly in identifying subtle temporal changes indicative of thyroid cancer. The proposed TSA-QDA model shows superior performance in terms of sensitivity, specificity, and classification accuracy for multi-class TIRADS classification.