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@article{171240,
        author = {Neha Banu H Harlapur},
        title = {Predicting Thyroid Disease Treatment Outcomes Using Machine Learning Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3131-3137},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171240},
        abstract = {Thyroid diseases, including hypothyroidism, hyperthyroidism, and autoimmune thyroid disorders, require careful management and personalized treatment strategies. In this study, we explore the use of machine learning (ML) models to predict treatment outcomes for thyroid diseases based on clinical and diagnostic data. By applying various ML algorithms such as KNN (K-Nearest Neighbors), Logistic Regression, Random Forest, decision trees, support vector machines (SVM) and XGBoost neural networks to patient records, we aim to identify the most effective treatments for patients, improve decision-making processes, and reduce trial-and-error approaches in thyroid disease management. The findings demonstrate that machine learning can play a significant role in enhancing treatment planning and optimizing outcomes for patients with thyroid disorders. The paper presents the findings of the machine learning models, comparing the prediction accuracy and other performance metrics. It may show how models like SVM or random forests outperform traditional statistical models or expert-based predictions.},
        keywords = {Machine learning, classification model, Thyroid diseases, KNN (K-Nearest Neighbors), Logistic Regression, Random Forest, Decision trees, Support vector machines (SVM) and XGBoost},
        month = {December},
        }