{"title":"An efficient approach for breast cancer classification using machine learning","authors":"Vedatrayee Chatterjee, Arnab Maitra, Soubhik Ghosh, Hritik Banerjee, Subhadeep Puitandi, Ankita Mukherjee","doi":"10.31181/jdaic10028012024c","DOIUrl":"https://doi.org/10.31181/jdaic10028012024c","url":null,"abstract":"Breast cancer, a life-threatening disease affecting millions worldwide, poses significant challenges due to its time-consuming manual determination process, potential risks, and human errors. It is a condition where cells of the breast develop unnaturally and uncontrollably, resulting in a mass called a tumor. If lumps in the breast are not addressed, they can spread to other regions of the body, including the bones, liver, and lungs. Early diagnosis is crucial for effective treatment and improved patient outcomes. In this research paper, we focus on employing machine learning models to achieve quick identification of breast cancer tumors as benign or malignant. The primary objective is to develop a decision-making visualization pattern using swarm plots and heat maps. To accomplish this, we utilized the Light GBM (Gradient Boosting Machine) algorithm and compared its performance against other established machine learning models, namely Logistic Regression, Gradient Boosting Algorithm, Random Forest Algorithm, and XG Boost Algorithm. Ultimately, our study demonstrates that the Light GBM Algorithm exhibits the highest accuracy of 96.98% in distinguishing between benign and malignant breast tumors.","PeriodicalId":508443,"journal":{"name":"Journal of Decision Analytics and Intelligent Computing","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sangeeta Phulara, Arun Kumar, Monika Narang, K. Bisht
{"title":"A novel hybrid grey-BCM approach in multi-criteria decision making: An application in OTT platform","authors":"Sangeeta Phulara, Arun Kumar, Monika Narang, K. Bisht","doi":"10.31181/jdaic10016012024p","DOIUrl":"https://doi.org/10.31181/jdaic10016012024p","url":null,"abstract":"Real world decision making is like a puzzle having complex, uncertain and vague information and this fact portray the wide range applicability of grey system theory in decision making procedure as grey system theory deals with the systems having information with uncertainty. In order to extend the base-criterion method to uncertain conditions, grey information may be a better way to solve a lot of multi-criteria decision-making problems. In this paper, we proposed a novel approach ‘grey base-criterion method’ (GBCM) based on the linguistic variables extended to the grey information. Weights of criteria have been calculated using GBCM. Numerical examples are illustrated and then the results are compared by the grey best-worst method (GBWM). Results of comparison show the high reliability of GBCM method with less consistency ratio over GBWM. A real case study of the fastest growing OTT (Over the Top) platforms in India has been taken to bestow the robustness of the proposed method.","PeriodicalId":508443,"journal":{"name":"Journal of Decision Analytics and Intelligent Computing","volume":" 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139619510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}