Enhanced Breast Cancer Prediction Using Advanced Machine Learning Techniques

Registration ID: IJNRD_226206

Published ID: IJNRD2408305

DOI: Click Here to Get

Gourav Kumar Dhiman , Er. Shivani Rana

Prediction Method, Breast Cancer, Effectiveness, Efficiency, Machine Learning.

As much as today breast cancer still ranks among some of the diseases that are common among women and are linked to clinical, lifestyle, and other social and/or economic factors. This means that employing machine learning, it is possible to predict breast cancer based on traits which are hidden deeply in data. In the case of a breast cancer detection in its preliminary stages, it will be wise to incorporate classification, clustering techniques, and feature selection among the other machine learning techniques. Reduce mortality and improve domestic and international health care delivery systems. The purpose of the research is therefore to develop a prediction model that will help in predicting the existence of breast cancer at its preliminary stage which is the use of classification and clustering. A great performance analysis of many health indicators, such as accuracy, True Positive Rate, False Positive Rate, Root Mean Square Error, precision, recall, etc., has been conducted.

Published Paper   E-Certificate

Gourav Kumar Dhiman & Er. Shivani Rana (August-2024). Enhanced Breast Cancer Prediction Using Advanced Machine Learning Techniques. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d44-d52. https://ijnrd.org/papers/IJNRD2408305.pdf

Volume 9 Issue 8, August-2024

Pages : d44-d52

Paper Reg. ID: IJNRD_226206

Published Paper Id: IJNRD2408305

Downloads: 000121981

Research Area: Computer Science & Technology 

Country: Hamirpur, Himachal Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408305.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408305

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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