Detection of Breast Cancer using Machine Learning Algorithms – A Survey

Harinishree, M. S.; Aditya, C.R.; Sachin, D. N.

doi:10.1109/iccmc51019.2021.9418488

Cited by 12 publications

(2 citation statements)

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“…It also gives freshers who want to evaluate ML algorithms all the information they need to grasp deep learning completely all the information they need to do so. In this study [11] compare three commonly used ML techniques for predicting cancer of the breast: NB, RF, and KNN. The WBCD data set was used as a training set to examine the effectiveness of several ML algorithms in terms of crucial characteristics like precision and accuracy.…”

Section: Related Workmentioning

confidence: 99%

Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique

Ansari,

Tripathi,

Ahmad

2024

Preprint

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Breast cancer continues to be a prominent issue in global health, requiring the implementation of novel approaches for the timely identification and assessment of the disease. Machine learning has been extensively integrated into the field of breast cancer diagnostics to gain profound insights and enhance the precision and efficacy of recognizing potential instances of breast cancer. Given the global nature of this disease, the early detection of cancer continues to pose a considerable problem. Our study introduces an ensemble strategy that integrates the results of Dimensionality Reduction (DR) approaches, namely Principal Component Analysis (PCA), Non-negative matrix factorization (NMF), and Value Decomposition (SVD), and subsequently inputs them into a resilient classification algorithm. In this study, we examine many algorithms, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP), to evaluate their diagnostic accuracy. Our findings show that MLP, LR, and SVM have a maximum accuracy of 97.9%, but MLP performance varies when used with NMF & PCA, which is 97.20%. LR also produced good accuracy with NMF and PCA, which is 97.9%, but again, performance is reduced when used with SVD. The SVM gives a consistent result with PCA, SVD, and NMF, which is 97.9%.

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Section: Related Workmentioning

confidence: 99%

Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique

Ansari,

Tripathi,

Ahmad

2024

Preprint

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“…Despite this, early detection has the potential to be a game-changer for various diseases since it can save patients, doctors, and even hospitals huge amounts of time and resources. The potential of AI in early diagnosis has been the subject of a significant amount of study [35,73,[80][81][82][83]. Early detection of certain diseases, such as cancer, can dramatically impact the course of treatment and recovery.…”

Section: Healthcare Activities Using Aimentioning

confidence: 99%

Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

et al. 2023

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The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows of healthcare organizations at an accelerated rate. This modification highlights the significant impact that AI has on a variety of tasks, especially in health procedures relating to early detection and diagnosis. Papers done in the past imply that AI has the potential to increase the overall quality of services provided in the healthcare industry. There have been reports that technology based on AI can improve the quality of human existence by making life simpler, safer, and more productive. A comprehensive analysis of previous scholarly research on the use of AI in the health area is provided in this research in the form of a literature review. In order to propose a classification framework, the review took into consideration 132 academic publications sourced from scholarly sources. The presentation covers both the benefits and the issues that AI capabilities provide for individuals, medical professionals, corporations, and the health industry. In addition, the social and ethical implications of AI are examined in the context of the output of value-added medical services for decision-making processes in healthcare, privacy and security measures for patient data, and health monitoring capabilities.

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