Applying Machine Learning Techniques To Predict Breast Cancer

Shilpa, K.; Adilakshmi, T.; Chitra, K Y

doi:10.1109/icps55917.2022.00011

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Popular machine learning algorithm called Decision Tree bases choices on feature values on a tree-like structure [4,5] . The dataset is divided recursively depending on the most useful attributes, creating a decision-making tree [6,7] .…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

K.P.Senthilkumar

2024

jes

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This research focuses on using machine learning to make breast cancer classification better. In this research various machine learning algorithm such as Decision Tree, Linear Discriminant, Support Vector Machine, K-Nearest Neighbors, Probabilistic Neural Network, Logistic Regression, Recurrent Neural Network, and Ensemble Method are used. We tested them using two different ways of splitting the data—90/10 and 70/30—and we also picked important features to consider. The Ensemble Method came in second place with accuracies of 98.2% and 97.6%. The Deep Neural Network performed really well too, with accuracies of 96.2% in the 90/10 split and 89.1% in the 70/30 split. We also found that selecting the right features improved accuracy a lot. This shows how important it is to choose the best features to make the models better. These results show that machine learning can be used to classify breast cancer effectively. The numbers prove that the Deep Neural Network and Ensemble methods have high accuracy, and selecting the right features makes them even better. The research outcomes introduces machine learning techniques that can improve breast cancer diagnosis, potentially changing the way doctors make decisions and improving patient outcomes.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

K.P.Senthilkumar

2024

jes

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“…In particular, for the purposes if testing, the anomaly is a Main types of lung cancer to be examined and detected. [4]- [6]. After extensive preprocessing of the dataset, and extraction of the features from the images, the models developed for the research will learn to differentiate between the various types of subtle abnormalities that are deemed typical ones of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

Jampani Chandra Sekhar

2024

jes

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The current research is aimed at studying the application of machine learning algorithms for the early prediction and detection of lung cancer taking the images of CT scans and MR scans. In this respect, the dataset from kaggle includes 3200 images from diverse sources, which are pre-processed and applied for the process of feature extraction, using traditional methods and deep learning systems, which include Convolutional Neural Networks, VGG-16, VGG-19, and RNN, while after the training the systems are evaluated in accordance with precision, recall, f1 score, and accuracy. As a result, it is evidenced that the best model is VGG-19 with the highest accuracy of 97.86%, while follows VGG-19 the VGG-16 the CNN model, and the RNN, implying effective implications for clinical practice. Certainly, the results of the research help to create a non-invasive, effective, and fast tool, using by clinicians in their practice. The use of machine learning algorithms for the process of early prediction and detection would be helpful for the timely treatment of patients and personalized treatment plans. In such a way, based on the current research, it could be summarized that the best models are constantly developed and gradually implemented in practice. However, more significant collaboration between the researchers, clinicians, and the industry is needed to have the full implementation of applied methods in practice, having fast results and timely actions taken by clinicians.

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“…The given project explores the prospects of ML in the domain of lung cancer detection and classification following the medical imaging dataset analysis. Nowadays, ML techniques have already proved to be efficient in diagnosing lung cancer in terms of both identifying and stages defining a wide selection of interstitial lung diseases [3]- [5]. The dataset analyzed in the given project encompasses the wide array of various 2400 images, representing lung cancer of different stages as well as non-cancerous data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches of Lung Cancer Image Processing for Detecting and Identifying Various Stages of Analysis

A. Harshavardhan

2024

jes

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The current paper is aimed at examining the use of machine learning approaches for lung cancer detection and classification using medical imaging data. In order to create the model, we collected a comprehensive dataset of 2400 images of lung cancer at different stages and healthy pictures. These data were preprocessed, and several approaches to the feature extraction were considered, namely Histogram of Oriented Gradients , and Local Binary Patterns . In addition, we attempted to use deep learning representations to determine their usefulness in this case. Moreover, these features were used for four ML models, namely Convolutional Neural Network , ResNet-18, , and VGG-19, to determine the most suitable one. To evaluate the general performance of these models, all the characteristic points were taken into account, such as the precision, recall, F1 score, accuracy, and confusion matrices. The results of the primary analysis indicate that the accuracy of our proposed model was the highest, 96.86%. The other places were taken by other deep learning architectures, which also demonstrate high level performance. In general, we may conclude that the findings show it is possible to use ML algorithms to improve the quality of clinical decisions and make the process of lung cancer detection and classification more accurate. At the same time, we were able to provide a comprehensive evaluation of all these results and the thorough analysis of the general performance of each model. This may serve as the basis for the subsequent improvements and changes that would allow enhancing the general quality of diagnostics and training more advanced models.

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Applying Machine Learning Techniques To Predict Breast Cancer

Cited by 12 publications

References 10 publications

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

Machine Learning Approaches of Lung Cancer Image Processing for Detecting and Identifying Various Stages of Analysis

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