Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

Gufran Ahmad Ansari, Salliah Shafi Bhat, Mohd Dilshad Ansari, Sultan Ahmad and Hikmat A. M. Abdeljaber

Data and Metadata, 2024, vol. 3, .346

Abstract: Introduction: One of the most common types of cancer and a significant contributor to the high death rates among women is breast cancer. It usually occurs in women. It is crucial to acquire a diagnosis early in order to kill cancer from becoming worse. Objective: The traditional diagnosing procedure takes more time. A fast and useful option can apply Machine Learning Technique (MLT) to identify illnesses. However new technology creates a variety of high-dimensional data kinds particularly when it comes to health or cancer data. Methods: Data classification techniques like Machine Learning are efficient. Particularly in the medical field where such techniques are often utilised to make decisions via diagnosis and analysis. Using Wisconsin Breast Cancer Dataset, the proposed research was carried out (WBCD). Some of these issues may be solved using the feature selection approach. Results: This research analyses the classification accuracy of different MLT: Logistic Regression, Support Vector Machine, and K-Nearest Neighbour. According to experiment results, SVM has the best accuracy of all algorithms, at 97.12%. Conclusion: The mentioned prediction models are based on several supervised MLT. Tenfold cross validation is applied. Additionally, author also proposed a Flow chart of breast Cancer using MLT.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.346:id:1056294dm2024346

DOI: 10.56294/dm2024.346

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