This paper is published in Volume-5, Issue-2, 2019
Area
Computer Engineering
Author
Shriya D. Narvekar
Co-authors
Ameya Patil, Jagruti Patil, Saniket Kudoo
Org/Univ
VIVA Institute of Technology, Virar, Maharashtra, India
Pub. Date
01 April, 2019
Paper ID
V5I2-1658
Publisher
Keywords
C5.0, Confusion matrix, F1 score, WPBC dataset, Xgboost, Breast cancer

Citationsacebook

IEEE
Shriya D. Narvekar, Ameya Patil, Jagruti Patil, Saniket Kudoo. Prognostication of breast cancer using data mining and machine learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shriya D. Narvekar, Ameya Patil, Jagruti Patil, Saniket Kudoo (2019). Prognostication of breast cancer using data mining and machine learning. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Shriya D. Narvekar, Ameya Patil, Jagruti Patil, Saniket Kudoo. "Prognostication of breast cancer using data mining and machine learning." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

In this study, WPBC that is Wisconsin Prognostic Breast Cancer (original) dataset to find an efficient predictor algorithm, to predict the recurring and non-recurring nature of breast cancer. Breast cancer is the most common disease found among the women, it is difficult for the physicians to know the exact reason behind breast cancer, and they need a smart system for predicting the illness on time before it is too late to be treated. It is one of the crucial reasons for death among females all over the world. The cancer tumor is generally categorized into benign and malignant tumors. Using machine learning and data mining techniques it can easily identify the cancer cells, provide benefits to the medical system. Use of classification algorithm C5.0 and XGBOOST sums up to have a considerably better result, improving the performance of the system. İn Classification, the calculation separates the information into unmistakable gatherings. It fundamentally pursues two stages of preparing first to learn and after that to a group.
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