Key result
Random Forest classifiers outperform other machine learning models for early PCOS detection and prediction.
Why the study?
Can machine learning algorithms accurately detect and predict PCOS from an optimal set of parameters?
Can machine learning algorithms accurately detect and predict PCOS from an optimal set of parameters?
Random Forest classifiers can effectively detect and predict PCOS using an optimized set of clinical parameters.
Requires prospective validation before clinical use; leaves open generalizability of Random Forest models for PCOS prediction.
Polycystic Ovary Syndrome (PCOS) is a medical condition which causes hormonal disorder in women in their childbearing years.The hormonal imbalance leads to a delayed or even absent menstrual cycle.Women with PCOS majorly suffer from excessive weight gain, facial hair growth, acne, hair loss, skin darkening and irregular periods leading to infertility in rare cases.The existing methodologies and treatments are insufficient for early-stage detection and prediction.To deal with this problem, we propose a system which can help in early detection and prediction of PCOS treatment from an optimal and minimal set of parameters.To detect whether a woman is suffering from PCOS, 5 different machine learning classifiers like Random Forest, SVM, Logistic Regression, Gaussian Naïve Bayes, K Neighbours have been used.Out of the 41 features from the dataset, top 30 features were identified using CHI SQUARE method and used in the feature vector.We also compared the results of each classifier and it has been observed that the accuracy of the Random Forest Classifier is the highest and the most reliable.The dataset used for training and testing is available on KAGGLE and owned by Prasoon Kottarathil.
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Vaidehi Thakre (2020) studied Polycystic Ovary Syndrome (PCOS). Machine learning classifiers (Random Forest, SVM, Logistic Regression, Gaussian Naïve Bayes, K Neighbours) vs. Comparison between different classifiers was evaluated on Accuracy of PCOS detection. A machine learning system using a Random Forest classifier achieved the highest and most reliable accuracy for the early detection and prediction of Polycystic Ovary Syndrome.
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