[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121453-en":3,"doc-seo-121453-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121453,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Enhancing Machine Learning Algorithm Performance for PCOS Diagnosis - Using SMOTENC on Imbalanced Data","Polycystic Ovarian Syndrome (PCOS) is a common endocrine disorder in women of reproductive age, often resulting from disruptions in hormonal regulation that affect menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is frequently diagnosed late and inaccurately, causing inappropriate treatment and long-term health complications. Machine learning can improve diagnostic accuracy, yet class imbalance in datasets weakens model reliability. This study applies SMOTENC to address imbalanced data and evaluates improvements across multiple algorithms.","ENHANCING MACHINE LEARNING ALGORITHM PERFORMANCE FOR PCOS DIAGNOSIS USING SMOTENC ON IMBALANCED DATA  \nRofiqoh Dewi1; Ratna Sri hayati1; Alfa Saleh2*; Dahri Yani Hakim Tanjung1; Abwabul Jinan3  \nBisnis Digital1  \nInformatika3  \nUniversitas Satya Terra Bhinneka, Medan, Indonesia1,3  \n[http://satyaterrabhinneka.ac.id](http://satyaterrabhinneka.ac.id1)[1](http://satyaterrabhinneka.ac.id1),3  \n[rofiqohdewi@satyaterrabhinneka.ac.id](rofiqohdewi@satyaterrabhinneka.ac.id); [ratnasrihayati@satyaterrabhinneka.ac.id](ratnasrihayati@satyaterrabhinneka.ac.id);  \n[dahritanjung@satyaterrabhinneka.ac.id](dahritanjung@satyaterrabhinneka.ac.id); [abwabuljinan@satyaterrabhinneka.ac.id](abwabuljinan@satyaterrabhinneka.ac.id)  \nInformatika2*  \nUniversitas Samudra, Aceh, Indonesia2  \n[http://unsam.ac.id](http://unsam.ac.id2)[2](http://unsam.ac.id2)  \n[alfasaleh@unsam.ac.id](alfasaleh@unsam.ac.id)*  \n(*) Corresponding Author  \n(Responsible for the Quality of Paper Content)  \nThe creation is distributed under the Creative Commons Attribution-Non Commercial 4.0 International License.  \nAbstract—Polycystic Ovarian Syndrome (PCOS) is one of the most frequently occurring endocrine disorders in women of reproductive age, distinguished by disruptions in hormonal regulation that can impact menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is often diagnosed late and inaccurately, leading to inappropriate treatment and long-term health issues for patients. Machine learning can serve as an effective solution to enhance the accuracy of PCOS diagnosis. However, one of the primary challenges encountered is the class imbalance in the dataset, where the number of positive case data (PCOS) is often significantly lower than the negative case data. This imbalance can result in a biased model that is less effective in predicting the actual condition of patients. In this study, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) method is recommended to address the issue of imbalanced data, thereby improving the performance and accuracy of the machine learning model employed. The evaluation matrix test results clearly demonstrate that the accuracy of each machine learning model improved after applying the SMOTENC method. Specifically, the accuracy of the K-Nearest Neighbors (KNN) algorithm increased from 81.6% to 89.8%, the Support Vector Machine (SVM) algorithm from 90.6% to 92.5%, the Naive Bayes algorithm from 70% to 82.3%, and the C4.5 algorithm from 99.6% to 99.7%. This research provides a substantial contribution to advancing the development of diagnostic methods thatare both more precise and efficient.  \nKeywords: imbalanced data, machine learning algorithm, PCOS, SMOTENC  \nIntisari—Sindrom Ovarium Polikistik (PCOS) merupakan salah satu gangguan endokrin yang paling sering terjadi pada wanita usia reproduktif, yang ditandai dengan gangguan pada regulasi hormon yang dapat memengaruhisiklus menstruasi, kesuburan, dan penampilan fisik. Meskipun prevalensinya tinggi, PCOS sering kali terlambat didiagnosis dan tidak akurat, sehingga menyebabkan pengobatan yang tidak tepat dan masalah kesehatan jangka panjang bagi pasien. Pembelajaran mesin dapat menjadi solusi yang efektif untuk meningkatkan akurasi diagnosis PCOS. Namun, salah satu tantangan utama yang dihadapi adalah ketidakseimbangan kelas dalam dataset, di mana jumlah data kasus positif (PCOS) sering kali jauh lebih rendah daripada data kasus negatif. Ketidakseimbangan ini dapat menghasilkan model yang bias dan kurangefektif dalam memprediksi kondisi pasien yang sebenarnya. Dalam penelitian ini, metode Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) direkomendasikan untuk mengatasi  \nmasalah ketidakseimbangan data, sehingga dapat meningkatkan kinerja dan akurasi model pembelajaranmesin yang digunakan. Hasil uji matriks evaluasi dengan jelas menunjukkan bahwa akurasi setiap model machine learning meningkat setela","cbCaij12L6pkMy2K","https://ap.wps.com/l/cbCaij12L6pkMy2K","pdf",1548185,1,9,"English","en",105,"# Introduction\n## Background of PCOS and diagnosis challenges\n## Health lifestyle factors and hormonal imbalance\n## Machine learning for PCOS diagnostic accuracy","[{\"question\":\"Why is PCOS diagnosis often delayed or inaccurate?\",\"answer\":\"PCOS is frequently diagnosed late and inaccurately, which leads to inappropriate treatment and long-term health complications.\"},{\"question\":\"What problem does this study address in machine learning for PCOS?\",\"answer\":\"The study addresses class imbalance in the dataset, where positive (PCOS) cases are much fewer than negative cases, potentially biasing the model.\"},{\"question\":\"How does SMOTENC improve model performance in this research?\",\"answer\":\"SMOTENC is recommended to handle imbalanced data by oversampling in a way suitable for nominal and continuous features, increasing accuracy across evaluated models.\"}]","Enhancing Machine Learning Algorithm Performance for PCOS Diagnosis - Using SMOTENC on Imbalanced Data | PDF",1785735729,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-machine-learning-algorithm-performance-for-pcos-diagnosis-using-smotenc-on-imbalanced-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-machine-learning-algorithm-performance-for-pcos-diagnosis-using-smotenc-on-imbalanced-data/121453/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is PCOS diagnosis often delayed or inaccurate?","Question",{"text":75,"@type":76},"PCOS is frequently diagnosed late and inaccurately, which leads to inappropriate treatment and long-term health complications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does this study address in machine learning for PCOS?",{"text":80,"@type":76},"The study addresses class imbalance in the dataset, where positive (PCOS) cases are much fewer than negative cases, potentially biasing the model.",{"name":82,"@type":73,"acceptedAnswer":83},"How does SMOTENC improve model performance in this research?",{"text":84,"@type":76},"SMOTENC is recommended to handle imbalanced data by oversampling in a way suitable for nominal and continuous features, increasing accuracy across evaluated models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]