TY  - JOUR 
AU  - Chaganti, Rajasekhar
AU  - Rustam, Furqan
AU  - Torre Díez, Isabel de la
AU  - Vidal Mazón, Juan Luis
AU  - Rodríguez, Carmen Lili
AU  - Ashraf, Imran
PY  - 2022
SN  - 2072-6694
UR  - https://uvadoc.uva.es/handle/10324/61044
AB  - Simple Summary: The study presents a thyroid disease prediction approach which utilizes random
forest-based features to obtain high accuracy. The approach can obtain a 0.99 accuracy to predict
ten thyroid diseases.
AB  - Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly,...
LA  - eng
PB  - MDPI
KW  - Machine learning
KW  - Aprendizaje automático
KW  - Thyroid Diseases
KW  - Tiroides - Enfermedades
KW  - Thyroid gland - Diseases - Diagnosis
KW  - Tiroides - Enfermedades - Diagnóstico
KW  - Endocrinology
TI  - Thyroid disease prediction using selective features and machine learning techniques
DO  - 10.3390/cancers14163914
ER  -