CATEGORY BOOSTING MACHINE LEARNING ALGORITHM FOR BREAST CANCER PREDICTION

Auteurs

  • 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

Mots-clés :

Breast cancer, Category boosting, Machine learning, Prediction model

Résumé

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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Publiée

2021-12-09

Numéro

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

Rubrique

Génie biomédicale

Licence

(c) Copyright Revue Roumaine des Sciences Techniques, Série Électrotechnique et Énergétique 2021

Creative Commons License

Ce travail est disponible sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.

Comment citer

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