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.
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Pub Date : 2024-01-16DOI: 10.31181/jdaic10016012024p
Sangeeta Phulara, Arun Kumar, Monika Narang, K. Bisht
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.
现实世界中的决策就像一个谜题，信息复杂、不确定且模糊，而这一事实说明灰色系统理论在决策过程中具有广泛的适用性，因为灰色系统理论处理的是具有不确定性信息的系统。为了将基本标准法扩展到不确定条件下，灰色信息可能是解决大量多标准决策问题的更好方法。本文提出了一种基于语言变量扩展到灰色信息的新方法 "灰色基准标准法"（GBCM）。使用 GBCM 计算了标准的权重。先举例说明，然后用灰色最佳-最差法（GBWM）对结果进行比较。比较结果表明，GBCM 方法可靠性高，一致性比 GBWM 低。对印度增长最快的 OTT（Over the Top）平台进行了实际案例研究，以证明所提方法的稳健性。
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