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Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

Computer Systems Science & Engineering 2023年第10期47卷 239-253页

作者： Mohamed Abouhawwash S.Sridevi suma christal mary sundararajan Rohit Pachlor Faten Khalid Karim Doaa Sami Khafaga Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Sri Ramachandra Faculty of Engineering&Technology Sri Ramachandra Institute of Higher Education and ResearchPorurChennai600116India Department of Information Technology Panimalar Institute of TechnologyChennai600123India Department of Computer Science and Engineering School of EngineeringMIT ArtDesign and Technology UniversityPune412201India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourahbint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of *** a method to aid doctors in this procedure was crucial due to the difficulties in detecting this *** research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and ***,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of *** this research,the tri-stage wrapper method is used because it reduces the computation *** proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this *** start,this research pre-processed the *** for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is ***,various classification methods are tested and *** learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for *** experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

关键词： Deep learning automatic detection polycystic ovarian syndrome tri-stage wrapper method mutual information relief Chi-Square

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