머신러닝을 이용한 다낭성 난소 증후군 분류 모델 평가
Evaluation of Polycystic Ovary Syndrome Classification Model Using Machine Learning
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In this paper, general characteristics, blood tests, and ultrasound examination results were used toclassify the presence of polycystic ovary syndrome (PCOS). The classification algorithms used were SVM (SupportVector Machine) and k-NN (k-Nearest Neighbors). Out of a total of 300 data samples, 210 were used as trainingdata and 90 as test data. The results showed that SVM achieved higher accuracy compared to k-NN, confirming itsgreater utility in diagnosing the presence of PCOS. Future research is expected to improve classification performanceby incorporating various additional indicators and securing more data. Additionally, it is expected to serve as afoundational resource for predicting and classifying other diseases
Abstract
In this paper, general characteristics, blood tests, and ultrasound examination results were used toclassify the presence of polycystic ovary syndrome (PCOS). The classification algorithms used were SVM (SupportVector Machine) and k-NN (k-Nearest Neighbors). Out of a total of 300 data samples, 210 were used as trainingdata and 90 as test data. The results showed that SVM achieved higher accuracy compared to k-NN, confirming itsgreater utility in diagnosing the presence of PCOS. Future research is expected to improve classification performanceby incorporating various additional indicators and securing more data. Additionally, it is expected to serve as afoundational resource for predicting and classifying other diseases
참고문헌 (11)
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