A Novel Machine Learning Framework for Prediction of Early-Stage Thyroid Disease Using Classification Techniques

Abstract

Thyroid disease is one of the most common diseases among the female Population in Bangladesh. Hypothyroid is a common variation of thyroid disease. It is clearly visible that hypothyroid disease is mostly seen in female patients. Most people are not aware of that disease as a result of which, it is rapidly turning into a critical disease. It is very much important to detect it in the primary stage so that doctors can provide better medication to keep itself turning into a serious matter. Predicting disease in machine learning is a difficult task. Machine learning plays an important role in predicting diseases. Again distinct Predicting techniques have facilitated this process analysis and assumption of diseases. There are two types of thyroid diseases namely Hyperthyroid and Hypothyroid. Here, in this paper, we have attempted to predict hypothyroid in the primary stage. To do so, we have mainly used classification algorithms named Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Naive Bayes (NB). By observing the results, we could extrapolate that our Trained (Structured) Dataset provide’s an (approx.) 97.05% accuracy for Random Forest (Bagging) classification algorithm.