Breast Cancer Prediction Using Machine Learning Techniques

Apoorva, V.; Yogish, H. K.; Chayadevi, M. L.

doi:10.2991/ahis.k.210913.043

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021) . [4] Combining advanced algorithms and comprehensive data, the system can improve early detection and better prevention in cancer treatment.…”

Section: Breast Cancer Prediction Using Machine Learning Techniquesmentioning

confidence: 99%

Breast Cancer Detection Using Machine Learning Alogrithm

Sagar

2024

IJSREM

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Breast cancer is the most common and second most common cause of cancer in women. [3] Early diagnosis of breast cancer can provide better treatment and thus increase survival rates. Data classification using machine learning is widely used in cancer diagnosis and early cancer detection. The purpose of this literature review is to focus on the use of machine learning in classifying existing data in the early detection and diagnosis of breast cancer. When much scientific literature is examined, it is clearly seen that there are many methods for the diagnosis of breast cancer. The aim of this study is to provide a comprehensive review and recommendations on cancer screening and diagnosis. "Breast cancer type classification using machine learning."[1]Provides an overview of current research on multiple breast cancer cases using data mining techniques to improve breast cancer diagnosis and prognosis.. Key Words: breast cancer, machine learning, artificial neural networks, decision tree, support vector machine nearest neighbor, healthcare system, Wisconsin breast cancer database

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Section: Breast Cancer Prediction Using Machine Learning Techniquesmentioning

confidence: 99%

Breast Cancer Detection Using Machine Learning Alogrithm

Sagar

2024

IJSREM

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“…LR achieves 98% accuracy. Apoorva et al [14] In order to evaluate how well the ML models performed, the authors utilized the 10-fold cross-validation technique. Gaurav Singh [15] The dataset used in the study is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection Technique using Machine Learning Classifiers

Guharoy,

Mehdi,

Hada

et al. 2023

2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon)

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Most common Cancer which is commonly found amongst women is Breast Cancer and if we talk about the cause of death then it rates the second highest. The most effective way to cure cancer is to track cancer in the early stages, so if detected early and treated properly it may get cured more effectively. To get the minimum error, the most important thing is to develop the technique which gives us the highest accuracy of prediction. In this research, we are using machine-learning algorithms that will be used to compare the accuracy here, we will present a prediction model using machine-learning models including LR, RF, DT, etc. are tested on the Breast Cancer Wisconsin dataset, and different datasets. By using the results of this research, the efficiency of various strategies can be defined, and the most precise technique may be applied to accurately forecast breast cancer. This research was conducted using a set of parameters, however, in the future suitable prediction techniques will be used.

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“…Apoorva V, Yogish H K, and Chayadevi ML [10] used machine learning and deep learning ideas to diagnose and cure breast cancer early. The purpose was to forecast and research cancer databases to improve accuracy by using convolutional neural networks on an image dataset with features extracted from photos of breast mass.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

Poornima,

Mamatha,

Krishnamai

et al. 2023

E3S Web Conf.

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Breast cancer is the most common cancer among women. It occurs when few breast cells begin to grow abnormally. The national average for 2022 is 100.4 cases per 1,00,000 people, with a large number of women being diagnosed with breast cancer. The objective is to design a prediction system that can predict breast cancer at early stages using a set of attributes that have been selected from a critical dataset. The Wisconsin Kaggle dataset is used for this experiment. The goal of this work is to predict breast cancer utilizing hybrid machine learning methodologies, such as SVM and PCA. ML algorithms that could help to predict cancer, as the early detection of this disease would help to slow down the progression of other diseases. In our paper, we are implementing Hybrid algorithms like PCA and SVM and optimizing SVM with k-fold cross-validation for predicting Breast cancer at early stages with high accuracy. The goal is to raise the fraction of early-stage breast cancer detection and to reduce mistake rates with maximum precision, which are sustainable.

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Breast Cancer Prediction Using Machine Learning Techniques

Cited by 6 publications

References 20 publications

Breast Cancer Detection Using Machine Learning Alogrithm

Breast Cancer Detection Using Machine Learning Alogrithm

Breast Cancer Detection Technique using Machine Learning Classifiers

Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

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