Machine Learning Approaches to Predict Breast Cancer: Bangladesh
Perspective
- URL: http://arxiv.org/abs/2206.14972v1
- Date: Thu, 30 Jun 2022 01:44:53 GMT
- Title: Machine Learning Approaches to Predict Breast Cancer: Bangladesh
Perspective
- Authors: Taminul Islam, Arindom Kundu, Nazmul Islam Khan, Choyon Chandra Bonik,
Flora Akter, and Md Jihadul Islam
- Abstract summary: This study focuses on finding the best algorithm that can forecast breast cancer with maximum accuracy in terms of its classes.
After implementing the model, this study achieved the best model accuracy, 94% on Random Forest and XGBoost.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, Breast cancer has risen to become one of the most prominent causes
of death in recent years. Among all malignancies, this is the most frequent and
the major cause of death for women globally. Manually diagnosing this disease
requires a good amount of time and expertise. Breast cancer detection is
time-consuming, and the spread of the disease can be reduced by developing
machine-based breast cancer predictions. In Machine learning, the system can
learn from prior instances and find hard-to-detect patterns from noisy or
complicated data sets using various statistical, probabilistic, and
optimization approaches. This work compares several machine learning
algorithm's classification accuracy, precision, sensitivity, and specificity on
a newly collected dataset. In this work Decision tree, Random Forest, Logistic
Regression, Naive Bayes, and XGBoost, these five machine learning approaches
have been implemented to get the best performance on our dataset. This study
focuses on finding the best algorithm that can forecast breast cancer with
maximum accuracy in terms of its classes. This work evaluated the quality of
each algorithm's data classification in terms of efficiency and effectiveness.
And also compared with other published work on this domain. After implementing
the model, this study achieved the best model accuracy, 94% on Random Forest
and XGBoost.
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