International Journal of Electrical, Electronics and Data Communication (IJEEDC)
eISSN:2320-2084 , pISSN:2321-2950
.
Follow Us On :
current issue
Volume-10,Issue-4  ( Apr, 2022 )
ARCHIVES
  1. Volume-10,Issue-3  ( Mar, 2022 )
  2. Volume-10,Issue-1  ( Jan, 2022 )
  3. Volume-9,Issue-12  ( Dec, 2021 )

Statistics report
May. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 111
Paper Published : 1546
No. of Authors : 4203
  Journal Paper


Paper Title :
A Machine Learning-Based Holistic Approach to Predict the Survival of Breast Cancer Patients

Author :Serhat Simsek, Ugur Kursuncu, Hatice Okcu, Eyyub Kibis, Ali Dag

Article Citation :Serhat Simsek ,Ugur Kursuncu ,Hatice Okcu ,Eyyub Kibis ,Ali Dag , (2018 ) " A Machine Learning-Based Holistic Approach to Predict the Survival of Breast Cancer Patients " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 9-13, Volume-6,Issue-4

Abstract : In the early stages of breast cancer, inefficient treatment methods, as well as the patient's health condition may impact the patient's lifetime expectancy. In this study, given a set of explanatory variables that include the patient's demographics, health condition, and cancer treatment regimen, our objective is to investigate the performance of four different machine learning methods including an artificial neural network, Support Vector Machines and Random Forests. To achieve this ultimate goal, we utilize these three methods with a ten-fold cross validation to predict the one year, five years, ten years and fifteen years survivability of the breast cancer patients after initial diagnosis. The results of each method are compared with respect to accuracy, sensitivity, specificity, and area-under-the-curve (AUC) metrics. According to the proposed methodology, we observe that the Random Forest method shows better performance when compared to the others in most of the evaluation criteria that have been used in this study. In addition, in all prediction models, the stage of the cancer has been determined as the most important predictor of breast cancer survivability. The current study can be utilized by the medical practitioners as well as medical researchers for potential prospective studies. Needless to say, such outcomes can be considered as a decision support mechanism, not as a primary decision maker. Index Terms – Artificial Intelligence, Breast Cancer, Data Mining, Machine Learning, Prediction

Type : Research paper

Published : Volume-6,Issue-4


DOIONLINE NO - IJEEDC-IRAJ-DOIONLINE-11935   View Here

Copyright: © Institute of Research and Journals

| PDF |
Viewed - 89
| Published on 2018-06-26
   
   
IRAJ Other Journals
IJEEDC updates
Volume-9,Issue-2(Feb,2021) Want to join us ? CLick here http://ijeedc.iraj.in/join_editorial_board.php
The Conference World

JOURNAL SUPPORTED BY

ADDRESS

Technical Editor, IJEEDC
Department of Journal and Publication
Plot no. 30, Dharma Vihar,
Khandagiri, Bhubaneswar, Odisha, India, 751030
Mob/Whatsapp: +91-9040435740