Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis

By Hiba Asri, H. Mousannif, H. A. Moatassime, Thomas Noël. ANT/SEIT, 2016

695 citations recorded by the source corpus.

Abstract Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. Experimental results show that SVM gives the highest accuracy (97.13%) with lowest error rate. All experiments are executed within a simulation environment and conducted in WEKA data mining tool.

Subfields: Artificial Intelligence, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/6b9b14e45b04184b552c0e6c2acac12d18471813

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Citations in the corpus

COVID-19 Future Forecasting Using Supervised Machine Learning Models (2020, background): “There are lots of studies performed for the prediction of different diseases using machine learning techniques such as coronary artery disease [4], cardiovascular disease prediction [5], and breast cancer prediction [6].”
Noisy Channel Language Model Prompting for Few-Shot Text Classification (2021, background): “The worst-case accuracy is likely of more interest in high-risk applications (Asri et al., 2016; Guo et al., 2017).”
Prompt-free and Efficient Few-shot Learning with Language Models (2022, background): “Therefore, we report average, worst-case performance, and standard deviation across all runs, where the last two values can be important for risk-sensitive applications (Asri et al., 2016).”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “ each party are different. Moreover, the health care data is strictly protected by laws. In health care systems, besides neural networks, a wide range of machine learning algorithms are commonly used [165] [166]. The learning process should be distributed, because each party shares data, trains the ”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, neutral): “edu/ml/datasets/parkinsons [58, 59, 60] Breast cancer WDBC dataset https://archive.”