Machine Learning‐Based Recommender System for Breast Cancer Prognosis

Kanimozhi, G.; Shanmugavadivu, P.; Rani, M. Mary Shanthi

doi:10.1002/9781119711582.ch7

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…The dataset was processed using a correlation matrix and a heatmap feature selection approach. A correlation matrix can be used to evaluate the degree of similarity between independent and dependent properties [26]. The heatmap provides a visual summary of the correlation matrix, making it easier to identify patterns and relationships between features.…”

Section: Figure 2 Data Distribution Of Tcga Brca Clinical Datamentioning

confidence: 99%

Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Pichai,

Kanimozhi,

Rani

et al. 2024

EAI Endorsed Trans Perv Health Tech

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Cancer, a malignant disease, results from abnormalities in the body cells that lead to uncontrolled growth and division, surpassing healthy growth and stability. In the case of breast cancer, this uncontrolled growth and division occurs in breast cells. Early identification of breast cancer is key to lowering mortality rates. Several new developments in artificial intelligence predictive models show promise for assisting decision-making. The primary goal of the proposed study is to build an efficient Breast Cancer Intelligent System using a multimodal dataset. The aim is to to establish Computer-Aided Diagnosis for breast cancer by integrating various data.This study uses the TCGA "The Cancer Genome Atlas Breast Invasive Carcinoma Collection" (TCGA-BRCA) dataset, which is part of an ongoing effort to create a community integrating cancer phenotypic and genotypic data. The TCGA- BRCA dataset includes: Clinical Data, RNASeq Gene Data, Mutation Data, and Methylation Data. Both clinical and genomic data are used in this study for breast cancer diagnosis. Integrating multiple data modalities enhances the robustness and precision of diagnostic and prognostic models in comparison with conventional techniques. The approach offers several advantages over unimodal models due to its ability to integrate diverse data sources. Additionally, these models can be employed to forecast the likelihood of a patient developing breast cancer in the near future, providing a valuable tool for early intervention and treatment planning.

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Section: Figure 2 Data Distribution Of Tcga Brca Clinical Datamentioning

confidence: 99%

Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Pichai,

Kanimozhi,

Rani

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…A positive value indicates a strong association, while a negative value indicates otherwise. This phase aims to determine the connection between the attributes [14]. As shown in Figure 3, the correlation heatmap visually represents the relationships within the resulting correlation matrix.…”

Section: Data Preprocessingmentioning

confidence: 99%

Automatic Data-Driven Classification Systems for Cardiovascular Disease

Jayaraman,

Pichai

2024

EAI Endorsed Trans Perv Health Tech

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Cardiovascular disease (CVD) continues to contribute significantly to preventable deaths and avoidable disability worldwide. Prediction and prevention are of utmost importance in the support of public health. Machine learning and deep learning algorithms have emerged as powerful tools to improve the accuracy of diagnosis, prognosis, and treatment of cardiovascular disease. By employing these technologies, medical professionals can gain valuable insights into the risk factors associated with CVD. The focus of this research is to classify and predict cardiovascular diseases using techniques such as support vector machines, ensemble methods, decision trees, random forests, and neural networks. The effectiveness of these algorithms is evaluated based on metrics including accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results show that support vector machines and ensemble methods offer superior accuracy, while neural networks exhibit higher sensitivity and specificity in predicting cardiovascular diseases.

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“…This method produced a better result of 91.37% accuracy. Kanimozhi et al 23 recommended ML‐based breast cancer prognosis using the ML prediction algorithms for health recommendations. It is evaluated using root mean square error and mean absolute error on a BCC dataset for the best prediction attribute with the best prediction algorithm.…”

Section: Related Workmentioning

confidence: 99%

Data imputation in deep neural network to enhance breast cancer detection

Ganesan

Shanmugavadivu

Kavitha

et al. 2022

Int J Imaging Syst Tech

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Breast cancer is one of the most precarious cancers that claims many women's' lives every year. The existing automated systems for mammography datasets are designed to detect the abnormalities and classify them as benign or malignant. However, these systems ignore the incompleteness of data due to missing or irrelevant values in the dataset, which severely impede the automated system's reliability. To enhance the reliability of the decision outcomes, this study proposes a composite imputed multilayer fully connected deep networks (MFCN) that can replace the incomplete values in the dataset to augment the

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Machine Learning‐Based Recommender System for Breast Cancer Prognosis

Cited by 10 publications

References 9 publications

Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Automatic Data-Driven Classification Systems for Cardiovascular Disease

Data imputation in deep neural network to enhance breast cancer detection

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