[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120863-en":3,"doc-seo-120863-105":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},120863,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Breast Tumor Prediction and Feature Importance Score Finding Using Machine Learning Algorithms - Research Report","Breast tumor prediction research builds an accurate machine learning framework to identify breast tumors and quantify how individual features influence classification decisions. The work collects and preprocesses the Wisconsin Breast Cancer original dataset, splits it into training and testing sets, and trains multiple algorithms including Random Forest, Decision Tree, Logistic Regression, Multi-Layer Perceptron, Gradient Boosting, and K-Nearest Neighbors. Models are evaluated with performance metrics and feature importance scores, validated via 10-fold cross-validation. Random Forest achieves 98.56% accuracy, and top features provide clinically relevant insight.","UDC 618.19-006:004 .942 doi: 10.32620/reks.2023.4.03  \nSk. Shalauddin KABIR1, Md. Sabbir AHMMED2, Md. Moradul SIDDIQUE1, Romana Rahman EMA1, Motiur RAHMAN2, Syed Md. GALIB1  \n1 Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore-7408, Bangladesh  \n2 Department of Computer Science and Engineering, NUBTK Khulna-9100, Bangladesh  \nBREAST TUMOR PREDICTION AND FEATURE IMPORTANCE SCORE FINDING USING MACHINE LEARNING ALGORITHMS  \nThe subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.  \nKeywords: Breast tumor; Benign; Classification model; Machine learning; Tumor; Malignant; Data optimization.  \nIntroduction  \nAn abnormal mass of tissue is referred to as a tumor. Excessive cell division and growth lead to the formation of tumors. Tumors may be benign or malignant. Benign tumors develop gradually and do not metastasize (spread to other portion of the body), it is not a cancerous tumor. Malignant tumors are abnormal growths of cells that can invade nearby tissues and spread to other parts of the body. Through the lymphatic and blood systems, it can also spread to other bodily areas and is called a neoplasm [1]. Cell division is the process by which human cells develop and reproduce. Cells grow and become old; they die. Again, new cells take their place and continue to work as workers for the human body. However, sometimes their work process breaks down and abnormal cells grow. These cells may turn into tumors, which are lumps of tissue and some are uncontrollable. These uncontrollable cells are called cancerous cells. Cancerous cells are also called malignant tumors [2] . The world is worried about women’s breast cancer.  \nThe most common form of cancer in women is breast cancer, which has several molecular characteristics [3]. In 2020, 2.3 million women were affected by breast cancer, with 68,500 deaths. As of the end of 2020, 7.8 million women had been diagnosed with breast cancer in the past 5 years [4] . This makes it the most common cancer on the planet.  \nMoreover, in developing countries, young women face more problems. To cope with this problem, early detection of tumors is the best way to obtain proper medical treatment. Therefore, we have u","cbCaihd2ZFYMOmOA","https://ap.wps.com/l/cbCaihd2ZFYMOmOA","pdf",949648,1,11,"English","en",105,"# Introduction\n## Breast tumor types and medical background\n## Breast cancer burden and need for early detection\n## Machine learning approach and problem framing\n## Dataset and feature description (FNA and WBCD)\n# Methods and Model Training\n## Data collection, preprocessing, and optimization\n## Algorithms evaluated\n## Training/testing evaluation and cross-validation\n# Results and Feature Importance\n## Model performance comparison\n## Top feature ranking and interpretation\n# Conclusions","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To develop an accurate predictive model for breast tumor identification and to compute feature importance scores to determine which features most affect the classification results.\"},{\"question\":\"Which dataset and sampling basis are used for the analysis?\",\"answer\":\"The study uses the Wisconsin Breast Cancer original dataset (WBCD), created from fine-needle aspiration (FNA) measurements that assign values (typically 1 to 10) to multiple clinical quantities.\"},{\"question\":\"Which machine learning model performs best and what accuracy is reported?\",\"answer\":\"Random Forest is reported as the most reliable predictor, achieving 98.56% accuracy under the evaluation setup.\"}]","Breast Tumor Prediction and Feature Importance Score Finding Using Machine Learning Algorithms - 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