Current Volume 9
Abstract
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection and accurate diagnosis play a crucial role in improving survival rates and enabling timely medical intervention. Traditional diagnostic methods often rely on manual examination and expert interpretation, which can be time-consuming and susceptible to human error. The rapid advancement of Machine Learning (ML) technologies has created new opportunities for developing intelligent systems capable of supporting healthcare professionals in disease diagnosis and clinical decision-making. This paper presents the implementation and deployment of a Web-Based Breast Tumor Detection System that utilizes the Random Forest machine learning algorithm to classify tumors as either benign or malignant. The proposed system is developed using the Wisconsin Breast Cancer Diagnostic Dataset, which contains various tumor-related clinical features such as radius, texture, perimeter, area, smoothness, and symmetry. The collected data undergoes preprocessing steps including feature validation, normalization, and label encoding to improve prediction performance and model reliability.
Keywords
Breast Cancer Detection, Tumor Classification, Machine Learning, Random Forest Classifier, Wisconsin Breast Cancer Dataset, Flask API, Next.js, Healthcare Analytics, Predictive Modeling, Clinical Decision Support System, Data Preprocessing, Web-Based Healthcare Application, Early Cancer Diagnosis, Artificial Intelligence in Healthcare.
Citations
IRE Journals:
Suyash Dhumne, Shripad Pawar, Datta Rahegaonkar, Chaitanya Hingmire, Dr. Lalit V. Patil "Tumor Detection Using ML" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2439-2446 https://doi.org/10.64388/IREV9I12-1719110
IEEE:
Suyash Dhumne, Shripad Pawar, Datta Rahegaonkar, Chaitanya Hingmire, Dr. Lalit V. Patil
"Tumor Detection Using ML" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1719110
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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