Machine Learning Approaches for Early Identification of Thyroid Disease

Abstract

Thyroid disease is becoming increasingly common around the world. Tension, infection, trauma, toxins, a low-calorie diet, and intake of excess medications have a negative effect on thyroid function. To resolve the issues of imbalanced thyroid function causing thyroid disease, it is more important to deal with the techniques which early identify the existence leading to saving lives. There are various treatments available but the majority of them require the involvement of long-term medication or major surgery. Machine learning helps in the accurate prediction of disease. In this work, the comparison of 4 classification models: Naive Bayes, Logistic Regression, KStar, and Decision tree (J48) has been done. The results show that all of the aforementioned classification models have an outcome in accuracy, while the J48 model has outperformed. For the implementation of the models, the dataset has been taken from Kaggle to build the model. In this research, each classification algorithm namely Naive Bayes, Logistic Regression, KStar, and J48 have produced accuracy values of 82.24%, 93.85%, 95.63%, and 97.80%, respectively. However, J48 has the highest accuracy, which shows that it is the best-performing model among all models.

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