SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning

Tiwari, Shamik; Kane, Lalit; Koundal, Deepika; Jain, Anurag; Alhudhaif, Adi; Polat, Kemal; Zaguia, Atef; Alenezi, Fayadh; Althubiti, Sara A.

doi:10.1016/j.eswa.2022.117592

Cited by 50 publications

(12 citation statements)

References 30 publications

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“…The reason is the implementation of MH algorithm to choose the clusters and the clustering process with RSA identified the cyst correctly. K-means with LS-SVM (K-M-SVM) [6], Random forest (RF) [7], and PCA with gradient boosting decision tree (PCA-GBDT) [28]. The comparative results in terms of accuracy are shown in Figure 11.…”

Section: Figure 9 Roc Comparison Resultsmentioning

confidence: 99%

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Sheikdavood¹,

Bala²

2023

ITC

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Polycystic ovary syndrome (PCOS) is a disorder in the female ovary caused because of reproductive age group hormonal changes. PCOS is a different follicle that is formed in the ovary and is termed an endocrine disorder. This disorder’s effects are often linked with clinical symptoms such as arteries, acne, hirsutism, diabetes, cardiovascular disease, and chronic infertility. It is mainly associated with type 2 diabetes, obesity with high cholesterol. This must be diagnosed at an earlier stage for avoiding other related diseases. To ensure infertility, various kinds of ovulatory failures must be significantly diagnosed and recognized. The physicians manually determine the PCOS using ultrasound images, but it is inefficient to declare whether it is a simple cyst, PCOS, or cancer cyst. This manual detection is prone to trying errors. In this paper, PCOS detection is performed through a series of processes such as preprocessing, segmentation, feature selection, and classification. The speckle noise is removed in preprocessing, and the images are enhanced for further processing. The proposed improved adaptive K-means with reptile search algorithm (IAKmeans-RSA) has been utilized for cyst segmentation and follicles recognition. The relevant features from the segmented images are extracted using a convolutional neural network (CNN). Finally, the classification is performed using the Deep Neural Network (DNN) approach. The proposed system efficiently diagnosed PCOS through cyst detection from the input images. The algorithm’s efficiency compared with existing methods shows that the proposed model is superior in segmenting and diagnosing PCOS.

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Section: Figure 9 Roc Comparison Resultsmentioning

confidence: 99%

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Sheikdavood¹,

Bala²

2023

ITC

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“…In [ 17 ], PCA with RF recorded an ACC of 89.02. In [ 6 ], RF with correlation recorded an ACC of 92.4. In [ 19 ], SVM with Pearson correlation recorded an ACC of 91.6.…”

Section: Discussionmentioning

confidence: 99%

“…The result showed that RF achieved the highest accuracy. In [ 6 ], the authors used correlation feature selection methodology to select a subset of features from the database. They applied different ML models: SVM, LR, RF, DT, KNN, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), GB, AdaBoost (AB), XGBoost (XB), and CatBoost, and obtained the optimal model based on correlation thresholds.…”

Section: Related Workmentioning

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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“… Type ML Classifiers Brief Description Examples of healthcare predictions References Bagging ensemble Random Forest classifier Integrates bootstrap aggregation (bagging) and random feature selection to create a set of decision trees with controlled variation that can anticipate the corresponding output activity class [79] PCOS detection, lymph disease diagnosis, thyroid disorder analysis etc. Tiwari et al [80] , Azar et al [81] , Mishra et al [82] Boosting ensemble Gradient Boosting classifier It is an ensemble forward learning model which eliminates all weaker predictors in favor of a stronger one using an upgraded version of the decision tree, in which each successor is selected using the refined structure score, gain computation, and advanced approximations [83] Lung cancer detection, diabetes diagnosis, Leukemia prediction etc. Chandrasekar et al [84] , Bahad et al [85] , Deif et al [86] eXtreme Gradient (XG) Boosting This approach is scalable and efficient form of gradient boosting that improves on two fronts: tree construction speed and a novel distributed algorithm for tree searches [87] Heart disease detection, chronic kidney disease diagnosis, breast cancer detection etc.…”

Section: Methodsmentioning

confidence: 99%

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Suha¹,

Islam²

2023

Heliyon

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SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning

Cited by 50 publications

References 30 publications

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

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

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

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