Diagnosis of polycystic ovary syndrome through different machine learning and feature selection techniques

Mehr, Homay Danaei; Polat, Hüseyin

doi:10.1007/s12553-021-00613-y

Cited by 46 publications

(21 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the previous studies in this area were based on traditional ML classifiers. However, recently a few researchers have focused on applying ensemble techniques in PCOS detection, but their exploration techniques are based on typical bagging, boosting or voting type of ensemble models [29] , [45] . To the best of our knowledge, the proposed technique based on stacking ensemble classification approach where both traditional as well as boosting or bagging ensemble models are aggregated to provide a stronger prediction is a unique solution in this domain.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, using feature selection strategies, the majority of previous studies randomly picked a specified number of features. For example, Bharti et al [65] applied ML classifiers with ten statistically significant features based on p-values, Inan et al [37] proposed to use most significant top twelve features, Danaei et al [29] had acquired best accuracy employing 28 features selected using Random Forest embedded feature selection technique and so on. However, hardly any study has investigated at how changing the numbers and combinations of features selected using that same feature selection method can affect the prediction result.…”

Section: Discussionmentioning

confidence: 99%

“…For example, to categorize between PCOS and non-PCOS criteria, Danaei et al [29] employed Extra Tree, Adaptive Boosting (AdaBoost), Bagging Ensemble with Random Forest and Multi-Layer Perceptron (MLP) classification models which were then evaluated through performance parameters using the reduced subgroups of features obtained by filter, embedded, and wrapper feature extraction techniques. For feature selection, Nasim et al [30] presented an improved chi-squared (CS-PCOS) mechanism and they then conducted a performance comparison analysis of ten hyper-parametrized machine learning models for PCOS prediction.…”

Section: Background Studymentioning

confidence: 99%

See 2 more Smart Citations

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

Suha¹,

Islam²

2023

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

See 1 more Smart Citation

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

Suha¹,

Islam²

2023

Heliyon

View full text Add to dashboard Cite

“…The study demonstrated that the performance of Bayesian classi er with 93.93% accuracy is better than the LR with 91.04% accuracy. Further, the authors in [23] indicate that the SVM outperforms other AI techniques with the prediction e cacy of 96.92%. In [24], Arti cial Neural Network has been employed to diagnose if a patient is suffering from PCOS with an accuracy of 87.96%.…”

Section: Literature Reviewmentioning

confidence: 97%

Computerized Diagnosis of Polycystic Ovary Syndrome Using Machine Learning and Swarm Intelligence Techniques

Subha

R²,

Radhakrishnan³

2022

Preprint

View full text Add to dashboard Cite

Polycystic Ovary Syndrome (PCOS) is one of the profound causes of infertility in women. Early detection, and treatment is essential in improving the prognosis in patients. The current conditions of fertility in India are skeptical, wherein women are at higher risk. PCOS is one of the major causes of infertility and scales upto 20% of women population in India. This requires a timely and accurate diagnosis which can be accomplished by developing automated diagnosing models. Having noted that the data to be dealt with consists of both clinical and non-clinical inputs, the effective information alone needs to be considered for the diagnosis. This necessitates an intelligent selection of features before diagnosing. Thus, swarm intelligence (SI) for feature selection and machine learning for classification is considered to develop a robust and efficient diagnostic model to detect PCOS condition. Initially, optimal features are selected using statistical approaches namely, correlation and Chi Square test and exhaustive search procedure by recursive elimination. Further, the SI algorithms, Particle Swarm Optimization (PSO) and Flashing firefly (FF) are attempted to identify the optimal number and feasible combination of features. Random forest classifier has been used in the ML model for classification. A comparative analysis of the results is discussed and validated based on the parameters accuracy of training and testing, precision, recall, F1-score, and AUC-ROC. The results reveal that ML models with different feature selection algorithms give best performance for different feature dimensions and the model with PSO based feature selection gives the highest performance with minimum feature size. Also PSO based algorithm evades the problem of redundancy in the feature subset.

show abstract

“…There is increased ratio of luteinizing hormone to follicle-stimulating hormone 2 . PCOS may also result in long-term complications such as high blood pressure, anxiety, depression, heart disease, type-2 diabetes, and endocrine disorders 3 .…”

Section: Introductionmentioning

confidence: 99%

Outcomes of Pioglitazone and Metformin on Anthropometric, Metabolic and Endocrine variables in POS

Khan¹,

Arslaan²,

Pahore³

et al. 2023

PJMHS

View full text Add to dashboard Cite

Aim: To evaluate the outcomes of Pioglita zone and Metformin on anthropometric, metabolic and endocrine variables in PCOS. Methods: This randomized controlled trial was executed at Pharmacology Department of Bahria University Medical and Dental College in collaboration with Mamji Hospital Karachi from September 2018 to March 2019. Eighty PCOS infertile patients as per Rotterdam 2003 criteria,aged 20-40 years were enrolled. They were divided into two groups of 40 patients each by using computer generated numbers. All patients had fasting insulin levels of >9 μU/mL, serum glucose levels ≥126 mg/dl and suffering from oligomenorrhoea or amenorrhoea. Group A received Tablet Pioglitazone 30 mg once daily and group B was given tablet Metformin 500 mg for three months along with lifestyle changes. Results: Sixty-one participants completed the study. Anthropometric variables included weight, BMI and W/H ratio. Metformin treated group reduced more weight and BMI as compared to Pioglitazone. Metabolic variables included glycaemic indices, lipid profile and hs-CRP estimation. Pioglitazone group improved glycaemic indices more compared to Metformin. Serum progesterone level was significantly increased in Pioglitazone group (5.20±1.18) than Metformin group. Pioglitazone produced more ovulation in comparison to Metformin. Hence it is proved that it is better option in those patients who are unable to tolerate Metformin and cannot continue treatment for infertility. Conclusion: Pioglitazone group produced highly significant increase in serum progesterone level indicating ovulation and improved insulin resistance than the metformin group. Keywords: Female infertility, ovulation induction, Metformin, Pioglitazone, polycystic ovarian syndrome

show abstract

Diagnosis of polycystic ovary syndrome through different machine learning and feature selection techniques

Cited by 46 publications

References 41 publications

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

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

Computerized Diagnosis of Polycystic Ovary Syndrome Using Machine Learning and Swarm Intelligence Techniques

Outcomes of Pioglitazone and Metformin on Anthropometric, Metabolic and Endocrine variables in POS

Contact Info

Product

Resources

About