[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118976-en":3,"doc-seo-118976-105":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":4,"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},118976,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Male Fertility Classification using Machine Learning and Oversampling Techniques - Slides","Machine learning methods are applied to male fertility diagnosis to enable early detection of infertility cases and support medical decision-making. This study conducts an experimental investigation on how oversampling and feature selection can improve shallow classifier performance using the Fertility Dataset. Two oversampling techniques, SMOTE and ADASYN, are combined with different scalers (MinMax and Standard) and feature selection approaches (SelectKBest and SelectFromModel). Results show higher model performance on the oversampled dataset. Random Forest achieves the best test results on the SMOTE set, with top accuracy and class-specific recall.","Male Fertility Classification using Machine Learning and Oversampling  \nTechniques  \nAGP Sidhawara1  \nDepartment of Informatics, Faculty of Industrial Technology, Universitas Atma Jaya Yogyakarta,  \nSleman 55282, Indonesia [Email:](Email:1aloysius.gonzaga@uajy.ac.id)[1](Email:1aloysius.gonzaga@uajy.ac.id)[aloysius.gonzaga@uajy.ac.id](Email:1aloysius.gonzaga@uajy.ac.id)  \nAbstrak. Klasifikasi Kesuburan Pria Menggunakan Machine Learning dan Teknik Oversampling. Metodepembelajaran mesin telah diterapkan pada diagnosis kesuburan pria dalam beberapa tahun terakhir. Melalui deteksi dini kasus infertilitas, penerapan teknologi ini menawarkan potensi manfaat di bidang medis. Studi ini menyajikan penyelidikan eksperimental yang mengkajiprospek penggunaan teknik oversampling dan pemilihan fitur untuk meningkatkan kinerja pengklasifikasi sederhana untuk mengklasifikasikan kesuburan pria pada Fertility Dataset. Dua teknik oversampling (SMOTE dan ADASYN), dua scaler berbeda (MinMax dan Standard), dan dua metode pemilihan fitur berbeda (SelectKBest dan SelectFromModel) digunakan untuk meningkatkan performa pengklasifikasi. Hasil menunjukkan bahwa performa model pembelajaranmesin lebih baikpada datasethasiloversampling dibandingkan datasetasli.  \nRandom Forest mencapaikinerja terbaikpada set tesSMOTEdenganakurasi 90%, Recall 89% dan 100% masing-masing di kelas Normal dan Altered. Fitur Kecelakaan atau Trauma, Usia, dan Demam Tinggi dipilih oleh SelectKBest, dan dianggap sebagaifaktor yang berkontribusi terhadapkesuburan pria dalampenelitian-penelitian sebelumnya.  \nKata Kunci: kesuburan laki-laki, klasifikasi, pembelajaranmesin, SMOTE, ADASYN  \nAbstract. Machine learning methods have been applied to male fertility diagnosis in recent years. Through early infertility case detection, this technology application offers potential benefits to the medicalfield. This study presentsan experimental investigation that examines the prospect of using the oversampling technique and feature selection to enhance the performance of shallow classifiers to classify male fertility on the Fertility Dataset. Two oversampling techniques (SMOTE and ADASYN), two different scalers (MinMax and Standard), and two differentfeatureselection methods (SelectKBestand SelectFromModel) were used to improve the performance of the classifier. The results show that the performance of machine learning models is better on the oversampled dataset than the original dataset. Random Forest performed best on theSMOTEtest set with 90% accuracy, 89% and 100% Recall in Normal and Altered classes, respectively. Accidents or trauma, Age, and High Fevers features are selected by SelectKBest, and considered as factors that contribute to male fertility in prior studies.  \nKeywords: male fertility, classification, machine learning, SMOTE, ADASYN  \n1. Introduction  \nThe capacity of a man to bear children or the ideal function of his reproductive organs for conception is known as fertility. Because of the complexity of the human reproductive system, appropriate ovulation and fertilization are essential for a healthy pregnancy. Infertility affects around 17.5% of adult people globally, or 1 in 6 of them, as reported by the World Health Organization (WHO) [1] . About 50% of infertility problems globally are caused by males [2] . Meanwhile, in Indonesia, 22.3% of couples experience infertility [3], with men between the ages of 30 and 40 having the highest rate [4] . The quality of semen, or sperm, is influenced by several triggering variables, including lifestyle choices, medical history, and physical trauma [5], [6] .  \nIn recent years, male reproduction health topics have utilized machine learning methods to diagnose male fertility [7]. A public dataset named Fertility Dataset was donated on 16 January 2013 at the UCI Machine Learning Repository [8] and has been widely used in machine learning research [9], [10], [11], [12], [13], [14] . Several shallow machine learning models such as the Support","cbCaiq6R2Wc1NDdZ","https://ap.wps.com/l/cbCaiq6R2Wc1NDdZ","pdf",232227,1,10,"English","en",105,"# Introduction\n## Fertility and infertility context\n## Dataset and prior studies\n## Imbalanced classes and oversampling\n## Feature selection approach\n# Literature Review","[{\"question\":\"What problem does this study address in male fertility classification?\",\"answer\":\"The study targets diagnosing male fertility using machine learning while addressing performance limitations caused by imbalanced classes in the dataset.\"},{\"question\":\"Which oversampling methods and feature selection techniques are evaluated?\",\"answer\":\"Two oversampling methods are tested: SMOTE and ADASYN. Two scalers (MinMax and Standard) and two feature selection methods (SelectKBest and SelectFromModel) are also evaluated to improve classifier performance.\"},{\"question\":\"Which model performs best and under what setting?\",\"answer\":\"Random Forest performs best on the test set created with SMOTE, achieving 90% accuracy and class-specific recall values of 89% for Normal and 100% for Altered.\"}]","Male Fertility Classification using Machine Learning and Oversampling Techniques - Slides | PDF",1785721297,25,{"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},"male-fertility-classification-using-machine-learning-and-oversampling-techniques-slides","",{"@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/male-fertility-classification-using-machine-learning-and-oversampling-techniques-slides/118976/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this study address in male fertility classification?","Question",{"text":75,"@type":76},"The study targets diagnosing male fertility using machine learning while addressing performance limitations caused by imbalanced classes in the dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which oversampling methods and feature selection techniques are evaluated?",{"text":80,"@type":76},"Two oversampling methods are tested: SMOTE and ADASYN. Two scalers (MinMax and Standard) and two feature selection methods (SelectKBest and SelectFromModel) are also evaluated to improve classifier performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and under what setting?",{"text":84,"@type":76},"Random Forest performs best on the test set created with SMOTE, achieving 90% accuracy and class-specific recall values of 89% for Normal and 100% for Altered.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]