A Comparison of Classification Methods of Hypothyroid Disease Prediction
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This paper proposes a comparison of classification methods of hypothyroid disease prediction using data mining techniques. A dataset from the UCI repository with the thyroid disease dataset is used to prepare data with missing value handling, imbalance class handling, and suitable attribute selection. Then, the dataset is used to build the model by comparing the performance of classification methods such as Multilayer Perceptron, Support Vector Machine, and Decision Tree. The result shows that the Decision Tree achieves high performance with an accuracy of 99.61%, which is higher than the Multilayer Perceptron and Support Vector Machine with an accuracy of 96.46 % and 92.93%, respectively. In addition, we compared the result with state-of-the-art, which uses a similar technique to our proposed method. The result shows that our proposed method also outperforms previous research. Therefore, we decided to use Decision tree model for the prototype system development in hypothyroid disease prediction to support physicians' decision-making for diagnosis and treatment. Furthermore, this paper proposes data visualization to help users for primary risk assessment of a chance of hypothyroid disease to acknowledge risk before deciding to meet physicians using demographic information. Therefore, it will reduce the cost of medical and death rates.
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