[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118020-en":3,"doc-seo-118020-105":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},118020,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification - Research Paper","Machine learning technology applies predictive models across healthcare to enhance decision quality, and model performance depends heavily on selecting suitable hyperparameters. This research tunes hyperparameters of multiple classifiers to distinguish benign from malignant breast abnormalities. Experiments use the Wisconsin Diagnosis Breast Cancer (WDBC) dataset and a fusion approach, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB), combined with Logistic Regression, Naive Bayes, and Support Vector Machine. Comparisons include SVM, NB, LR, KNN, Random Forest, and Decision Tree using precision, recall, specificity, F-measure, accuracy, TPR, and FPR.","Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification  \nSreekala K Ka, Jayakrushna Sahoob, Amarjit Royc  \na Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kerala, India,  \n[sreekalaphd2019@iiitkottayam.ac.in](sreekalaphd2019@iiitkottayam.ac.in)  \nb Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kerala, India,  \n[jsahoo@iiitkottayam.ac.in](jsahoo@iiitkottayam.ac.in)  \nc Department of Electrical Engineering (ECE Specialization), Ghani Khan Choudhury Institute of Engineering and Technology, West Bengal,  \nIndia, [amarjit@gkciet.ac.in](amarjit@gkciet.ac.in)  \nAbstract: Computer technology has employed Machine Learning models in a variety of applications to improve performance. The hyperparameter of a machine learning model must be adapted to overcome learning limitations and increase its performance. In this research, the hyperparameters of machine learning classifiers are tuned to identify cases of benign or malignant breast abnormalities. An experimental investigation was conducted using the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. A fusion model, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB) is employed, which is combined with conventional classification approaches like Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) . The proposed methods are compared to cutting-edge models like SVM, NB, LR, KNearest Neighbour (KNN), Random Forest, and Decision Tree using a wide range of parametric measures, such as Precision, Recall, Specificity, F-measure, Accuracy, True Positivity Rate (TPR), and False Positivity Rate (FPR) . The results show that the proposed methods outperform the leading models.  \nKeywords: Machine Learning, Hyperparameter, supervised Learning, Classification, optimization, Breast Cancer.  \n1. Introduction  \nMachine Learning models are excellent choice for breast cancer detection due to its adaptability, repeatability, and ability to maintain original data accuracy [1] . In medical field, machine learning is vital for various purposes, including disease diagnosis, prevention, health check-ups, major disease screenings, health management, early diagnosis, disease severity evaluation, treatment methodselection, treatment effect evaluation, and recovery[2-3] .  \nBreast cancer is a major health concern for women in Indian metropolitan cities too, with high rates of morbidity and mortality[4] . India is estimated to carry one third of the global breast cancer burden. Between 2008 and 2018, the incidence and mortality rates of breast cancer in India increased by 11.5% and 13.82% respectively, which may be attributed to a lack of breast cancer screening programs, late diagnosis, and inadequate medical facilities. Breast cancer is a heterogeneous tumor with diverse clinical characteristics and response to treatment, and tumors are complex tissues made up of various cell types that interact with each other. The normal cells in the tumor-associated stroma actively participate in carcinogenesis, contributing to cancer hallmarks. The course of the disease is strongly influenced by local microenvironmental factors, as well as systemic factors such as age, body mass index, menopausal status, and overall immunity.  \nIn the case of advanced classification methods such as Artificial Neural Network (ANN) and SVM, the classification's performance is affected largely by the dimension of feature vectors; also, the training time of the protocol is determined. A crucial task for feature selection is to extract and select required and useful features. After the selection of features, they are fed into a classifier, which categorizes the available lesions as benign or malignant. Punitha, Al-Turjman, and Stephan [5] proposed an automated breast cancer detection method using feature extraction and parameter optimization in ANNs. Some techniques are classifying lesions","cbCair5qmUuKbAPU","https://ap.wps.com/l/cbCair5qmUuKbAPU","pdf",749279,1,13,"English","en",105,"# Introduction\n## Breast cancer detection with machine learning\n## Feature selection and classification workflow\n## Optimization and hyperparameter tuning\n## Evaluation motivation for medical decision support","[{\"question\":\"Why is hyperparameter tuning important for mammogram classification?\",\"answer\":\"Model hyperparameters must be adapted to reduce learning limitations and improve classifier performance on specific datasets.\"},{\"question\":\"What dataset and fusion method are used in the experiments?\",\"answer\":\"Experiments use the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, and the fusion model BOHB-NB (Bayesian Optimization Hyper Band-Naïve Bayes) is combined with conventional classifiers.\"},{\"question\":\"How is performance evaluated in the study?\",\"answer\":\"Methods are compared using parametric measures including Precision, Recall, Specificity, F-measure, Accuracy, TPR, and FPR.\"}]","Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification - 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