Improving Accuracy for Classification of Thyroid Disorders using Machine Learning Algorithm

Thyroid disorders are common endocrine conditions that affect millions of people worldwide. Accurately identifying these disorders is essential for effective treatment and management. Traditional diagnostic methods, such as laboratory tests and clinical evaluations, are reliable but can be time-consuming and sometimes affected by human error. With advancements in artificial intelligence and machine learning (ML), automated systems are now being developed to improve diagnostic accuracy and efficiency. This study aims to build a machine learning model that classifies thyroid disorders using the thyroidDF.csv dataset. Several ML algorithms, including Random Forest, Artificial Neural Networks (ANN), and XGBoost, are applied to analyze patient data. Various data preprocessing techniques, such as feature selection, data balancing, and hyperparameter tuning, are used to enhance model performance. Among these models, XGBoost performs the best, showing higher accuracy, recall, and precision compared to others. Beyond classification, this research also addresses challenges like data imbalance and the need for model interpretability in medical applications. The proposed system is designed to be practical and reliable for clinical use, helping healthcare professionals make informed decisions for early detection and diagnosis. Future improvements could include expanding the dataset, incorporating explainable AI techniques, and developing real-time monitoring features to further improve accuracy and usability in real-world settings. This report is structured into chapters covering the motivation, methodology, experimental results, and future directions of this research. By integrating AI with medical diagnostics, this study contributes to improving healthcare solutions, enabling more precise diagnoses and better treatment strategies for thyroid disorders.