| Autor: |
Shaik, Mohammed Ali, Pappula, Praveen, Kumar, T. Sampath, Chiranjeevi, Battu |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2971 Issue 1, p1-8, 8p |
| Abstrakt: |
In the present era most of the people are effected with hypothyroid disease. People who are of age 13 and above are more effected with this disease and day by day it is transforming into a dangerous disease. The prediction of disease at earlier stage is very crucial so superior treatment is contributed by doctors. In this paper, hypothyroid disease prediction is implemented through Ensemble Machine Learning Algorithms and through RapidMiner Tool. The algorithms like Random Forest, AdaBoost, and Gradient Boosting and Bagging are implemented. These models were compared and evaluated using evaluation metrics like Accuracy, Precision, and Recall. As multiple models are combined so ensemble method is more robust and accurate. It removes unrelated data in the medical dataset and produces accurate and precise data. It proves that the ensemble method is very efficient than using a single classifying method. Hence implementing the ensemble method, we can predict the patient's disease efficiently. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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