Ghazalnaz Sharifonnasabi, F., and Makhdoom, Iman (2024) Comparison of Deep Learning and Machine Learning Algorithms to Diagnose and Predict Breast Cancer. In: Lecture Notes in Networks and Systems (839) pp. 31-43. From: ICITA 2023: 17th International Conference on Information Technology and Applications, 20-22 October 2023, Turin, Italy.

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Abstract

Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It is important to be aware of the risk factors for breast cancer and to get regular checkups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: multilayer perceptron (MLP) and convolutional neural network (CNN). We also considered the five machine learning algorithm titled: decision tree (DT), Naïve Bayesian (NB), support vector machine (SVM), K-nearest neighbors (KNN) algorithm, and eXtreme Gradient Boosting (XGboost) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms, and discovering of the most effective with respect to confusion matrix, accuracy, and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the VSCode environment based on Python programing language.

Item ID:	86154
Item Type:	Conference Item (Research - E1)
ISBN:	978-981-99-8324-7
Copyright Information:	© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Date Deposited:	04 Dec 2025 02:10
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 50% 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 50%
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