Polycystic Ovary Disease (PCOD) or Polycystic Ovary Syndrome (PCOS) is a hormonal lopsidedness issue found in women of reproductive age. In today’s generation, there has been a constant rise in the occurrence of this disease due to the hindered lifestyle routines and adulterated food. There are three types of ovaries and is classified as normal ovary, cystic ovary and polycystic ovary.(Child et al., 2001) Poly Cystic Ovarian Syndrome or PCOS is a complex hormonal plights distressing up to around 1 in every 10 women at their conceptive age. PCOS manifests during adolescence and is formed as a result of hormonal disturbances (Witchel et al., 2015). Peripherally inside the ovary, fluid-filled sacs are present which are called follicles or cysts. A polycystic ovary (PCO) can be characterized by twelve or more follicles with a diameter of 2-9 mm(Jarrett et al., 2020). PCOS affects both health and the quality of women's life. The symptoms include cardiovascular diseases, failure to ovulate and infertility, late menopause, type 2 diabetes, acne, baldness, hair loss, hirsutism, obesity, anxiety, depression, and stress(Khomami et al., 2015).
The early diagnosis and treatment can be used to control based on the symptoms and by the prevention of long-term problems. PCOS can be detected through ultrasonography by a doctor by reckoning the number and size of follicles situated in the ovaries. However, this process takes a protracted interval, need good image quality and high accuracy to detect the presence of PCOS (Balen et al., 2003). Another approach for PCOS detection is through biochemical parameters such as hormone levels examination. Since hormone examination is very expensive, other clinical parameters such as body mass index (BMI), menstrual cycle length, etc. are taken into consideration for the detection of PCOS (Ranjzad et al., 2011).
Among the plentiful problems that subsisted around us, the problems that are pertinent to the conceptive health of women was chosen as an area of our relevance, due to its materiality in this contemporary society. A exhaustive survey of studies on PCOS and systems for its supportive diagnosis was carried out. about 5-10% of Indian women in reproductive age are attained by the multifaceted endocrine disorder called Polycystic Ovary Syndrome (PCOS)(Denny et al., 2019). It is a supreme instigation of an ovulatory infertility and increases the risk for insulin resistance, obesity, cardiovascular disease and psychosocial irregularities (Pauli et al., 2011). The symptoms for PCOS might be divaricate from patient to patient. Some of them are inconsistency in menstrual periods, acne, overweight, increased tendency for infertility, intense hair fall, balding of front head, increased growth of facial hair(Ding et al., 2017). Traditionally the PCOS can be questionable when number of follicles in an ovary is more than 12 per unit area and visible in radiological scan (Kenigsberg et al., 2015). Few recent studies are preempted in elemental research direction, scrutinising the affiliated aspects such as obesity(Kalantar-Zadeh et al., 2005) and genetic factors(McCrory et al., 2010). In recent years, machine learning (ML) classification and feature selection algorithms have been used by researchers and clinicians for the prediction of diseases as a non-invasive method (Dutta et al., 2021). PCOS datasets which consist of heterogeneous attributes related to biochemical, clinical, medical history, symptoms of the patients and ultrasound images are used to build predictive models (Vagios et al., 2021). Various machine learning model used to detection & prediction of PCOS (Boomidevi & Usha, 2021). SPSS V 22.0 software used to identify the potential attributes out of 23. The best model Random Forest achieves maximum accuracy of 89.02%. An effective classification performance for PCOS, datasets are resample using a combination of SMOTE(Synthetic Minority Oversampling Techniques) & ENN (Edited Nearest Neighbor) (Khan Inan et al., 2021).From the experimental results it was seen that for a 10 fold cross validation Extreme Gradient Boosting classifier outperformed than all other classifiers. A system was proposed to classify PCOS on ultrasound images using the feature extraction (Gabor Wavelet method) and Competitive Neural Network (CNN) (Dewi et al., 2018). Highest accuracy & testing time of the proposed model were 80.84% and 60.64 seconds respectively. Diagnose of PCOS data five different machine learning approaches were applied (Hassan & Mirza, 2020). From the Result analysis it was seen that maximum accuracy obtain by random Forest is 96%. A novel hybrid structure to determine the chances of PCOS that coalesce navies Bayes and artificial neural network algorithm to produce the best result (Thomas & Kavitha, 2020). A comparative study was performed on two different tool Rapid Miner & Python, from the result analysis it was seen that Random Forest outperformed than the other classification when it was utilized in Rapid Miner (Satish et al., 2020).
Four classification algorithms and five feature selection methods for PCOS dataset was performed to predict the disease(Boomidevi & Usha, 2021; Sumathi et al., 2021; Wagha et al., 2020).
In this paper, our objective is to know the better performance machine learning classification algorithm after applying the Principle Component Analysis (PCA) & Synthetic Minority Oversampling Technique (SMOTE). The left over paper has been collaborated in the following way Section 2 emphasises the proposed framework which is being propounded for the coordination of the given dataset. Section 3 bouncing off and sorts out classification results that are handed for getting a result of the application of the proposed methodology. Section 4 puts the lid on for the findings of the proposed work in an trenchant way.