CATEGORY BOOSTING MACHINE LEARNING ALGORITHM FOR BREAST CANCER PREDICTION

Authors

  • HARSHIT GUPTA School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India Author
  • PRITAM KUMAR School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India Author
  • SHUBHAM SAURABH School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India Author
  • SUNIL KUMAR MISHRA School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India Author
  • BHARGAV APPASANI School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India Author
  • AVADH PATI Department of Electrical Engineering, National Institute of Technology, Silchar, India Author
  • CRISTIAN RAVARIU Faculty of Electrical, Electronic & Computer Engineering, Universitatea Politehnica Bucuresti, București-060042, Romania Author
  • AVIRENI SRINIVASULU Dept. of Electronics and Communication Engineering, K R Mangalam University, Gurgaon, India Author

Keywords:

Breast cancer, Category boosting, Machine learning, Prediction model

Abstract

Cancer is considered the worst of all diseases. It is a category of diseases that enable irregular growth that may enter or spread to certain body areas. These contrast with healthy, not multiplying tumors. There are 100 different cancer forms that impact humans. With the emergence of machine learning (ML), its uses have been identified in many fields, particularly medical research. It also used for cancer detection when a correct dataset is available. This paper suggests a category boosting (CatBoost) ML algorithm for predicting the different stages of breast cancer, facilitating early diagnosis. The proposed CatBoost algorithm is an efficient method to train and test the available data. To show the CatBoost method's efficacy a detailed comparative analysis has been carried out with other prominent ML approaches. It has been established that the CatBoost is accurate compared to the other ML methods.

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Published

09.12.2021

Issue

Vol. 66 No. 3 (2021): RRST-EE

Section

Génie biomédical | Biomedical Engineering

License

Copyright (c) 2021 Romanian Journal of Technical Sciences, Electrical and Energy Series

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

CATEGORY BOOSTING MACHINE LEARNING ALGORITHM FOR BREAST CANCER PREDICTION. (2021). REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 66(3), 201-206. https://journal.iem.pub.ro/rrst-ee/article/view/21