BACKGROUND
With the prolonged period of the Covid-19 global pandemic, artificial intelligent and digital health care have substantially advanced to improve and enhance diagnosis and treatment. In this study, we discuss development of a digital prediction tool for diagnosis of Polycystic Ovary Syndrome (PCOS) using machine learning techniques.
OBJECTIVE
We aim to develop a self-diagnostic prediction digital platform for Polycystic Ovary Syndrome based on noninvasive measures such as age, lifestyle, and anthropomorphic measures and symptoms, that do not require any lab or ultrasound results.
METHODS
In this retrospective study, a publicly available dataset of 541 women’s health information collected in Kerala, India, including PCOS status was acquired and used for analysis. Principal component analysis and K-means clustering are adopted to classify the sample into four subgroups based on anthropomorphic measures. The prediction for PCOS based on noninvasive measures is made on each subgroup using random forest classifiers and the prediction errors are estimated. Important predictors for diagnosing PCOS are identified for each subgroup.
RESULTS
By adopting subgroup models, we substantially improve the prediction error rates by 11.84% across the subgroups compared to using one model to the entire sample. The mean precision, sensitivity, accuracy and F1-score for the diagnosis of PCOS using the proposed subgroup models are 0.950, 0.972, 0.940 and 0.961, respectively. No invasive measures are used in this prediction.
CONCLUSIONS
Anthropomorphic measures are found to be important variables to classify the women into subgroups. The results of the proposed subgroup models suggest that more accurate prediction for the diagnosis of PCOS can be made when different models are used for different subgroups rather than when a single model is used for the whole sample. This work enables women to conveniently access the proposed self-diagnosis prediction platform at home without delay before they seek for further medical care.
CLINICALTRIAL
This is an observational study.
