Comparative Analysis of Machine Learning Algorithms for Prediction of PCOS

Chauhan, Preeti; Patil, Pooja; Rane, Neha; Raundale, Pooja; Kanakia, Harshil

doi:10.1109/iccict50803.2021.9510128

Cited by 20 publications

(4 citation statements)

References 6 publications

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“…In [ 21 ], RF with chi-square recorded an ACC of 90.9. In [ 22 ], DT with Gini importance recorded an ACC of 92.59. In [ 25 ], multi-stack of ML recorded an ACC of 98.…”

Section: Discussionmentioning

confidence: 99%

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Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence

et al. 2023

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Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healthcare systems to reduce the disease’s problems and complications. Machine learning (ML) and ensemble learning have recently shown promising results in medical diagnostics. The main goal of our research is to provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations. Feature selection methods with different types of ML models (logistic regression (LR), random forest (RF), decision tree (DT), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), xgboost, and Adaboost algorithm to get optimal feature selection and best model. Stacking ML models that combine the best base ML models with meta-learner are proposed to improve performance. Bayesian optimization is used to optimize ML models. Combining SMOTE (Synthetic Minority Oversampling Techniques) and ENN (Edited Nearest Neighbour) solves the class imbalance. The experimental results were made using a benchmark PCOS dataset with two ratios splitting 70:30 and 80:20. The result showed that the Stacking ML with REF feature selection recorded the highest accuracy at 100 compared to other models.

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“…In [ 21 ], RF with chi-square recorded an ACC of 90.9. In [ 22 ], DT with Gini importance recorded an ACC of 92.59. In [ 25 ], multi-stack of ML recorded an ACC of 98.…”

Section: Discussionmentioning

confidence: 99%

“…In [ 22 ], the authors used Gini importance to select features. They applied different ML models: KNN, DT, SVM, LR, and NB, to detect PCOS.…”

Section: Related Workmentioning

confidence: 99%

Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence

et al. 2023

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“…In study proposed by [6] Preeti Chauhan Et.al, DT classifier gives out most accurate results. In another such paper the authors used Bayesian and Logistic Classifiers in their study, and the comparison showed that BC with 93.93% accuracy outperformed LC with 91.04% accuracy [7].…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

Yadav,

Pande

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods. OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage. METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms. RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively. CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS.

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“…In here, the data is standardized for further use. [7] Next, strong training datasets are created and the model is trained to produce higher accuracy. Then, the model is tested with testing datasets.…”

Section: Introductionmentioning

confidence: 99%

PCOS Disease Prediction Using Machine Learning Algorithms

M.Priyadharshini,

A.Srimathi,

C.Sanjay

et al. 2024

Int Res J Adv Engg Hub

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Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting reproductive-aged women, characterized by hormonal imbalances, irregular menstrual cycles, and the presence of cysts on the ovaries. One of the most worrying diseases today is Polycystic Ovarian Syndrome (PCOS) which is highly dangerous to an extent of affecting women's reproductive life to a huge extent. The dataset includes a diverse set of features such as age, body mass index (BMI), hormonal levels, menstrual irregularities, and lifestyle factors. We explore various machine learning algorithms, including linear regression, decision tree, and random forests to identify the most effective model for PCOS prediction. [1] The study focuses on the development of a robust and clinically applicable predictive model that can aid healthcare professionals in early identification of individuals at risk of PCOS. The results obtained from this research have the potential to significantly impact the field of women's health by offering a reliable and efficient tool for PCOS prediction. Early identification of individuals at risk can facilitate timely interventions, personalized treatment plans, and improved outcomes. Furthermore, the study contributes to the growing body of literature on the application of machine learning in healthcare, demonstrating its potential as a valuable tool for predictive modelling in complex endocrine disorders such as PCOS.

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Comparative Analysis of Machine Learning Algorithms for Prediction of PCOS

Cited by 20 publications

References 6 publications

Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence

Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence

Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

PCOS Disease Prediction Using Machine Learning Algorithms

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