Thyroid disease treatment prediction with machine learning approaches
| Autor: | Immacolata Cristina Nettore, Martina Iammarino, Marta Cimitile, Mario Luca Bernardi, Lerina Aversano, Paolo Emidio Macchia, Chiara Verdone |
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| Přispěvatelé: | Watrobski J., Salabun W., Toro C., Zanni-Merk C., Howlett R.J., Jain L.C., Jain L.C., Aversano, L., Bernardi, M. L., Cimitile, M., Iammarino, M., Macchia, P. E., Nettore, I. C., Verdone, C. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
endocrine system
Computer science Medical information Machine learning computer.software_genre Classifier Anterior region Clinical history medicine General Environmental Science business.industry Thyroid disease Thyroid K-cross validation medicine.disease medicine.anatomical_structure General Earth and Planetary Sciences Artificial intelligence business Prediction computer Thyroid treatment Endocrine gland Hormone Levothyroxine Sodium |
| Zdroj: | KES |
| Popis: | The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists’ activities and improve the quality of the patients’ life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the ”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient’s treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%. |
| Databáze: | OpenAIRE |
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