Prediction, Detection and Recurrence of Breast Cancer Using Machine Learning Based on Image and Gene Datasets

Thakur, Bharti; Kumar, Nagesh

doi:10.1007/978-981-16-8248-3_21

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…where 𝔵 𝑚 𝑖 denotes the current flower 𝔵 𝑚 at ith iteration with 𝛾() is the standard gamma function with a constant c. Similarly, the next position of a flower in the case of local search can be defined as (4).…”

Section: Flower Pollination Algorithmmentioning

confidence: 99%

“…The use of machine learning (ML) and data mining (DM) for relapse prediction in breast cancer is thus an important area of research. In order to glean meaningful insights from large data sets, data miners employ statistical, probabilistic, and ML techniques [3], [4]. The ability to accurately forecast breast cancer helps doctors tailor treatments to the needs of each patient, improving care and survival rates.…”

Section: Introductionmentioning

confidence: 99%

“…The utilization of healthcare resources for these individuals is also enhanced. Why breast cancer recurs so rapidly is still a mystery to clinical and statistical researchers [4]. Researchers are urged to use ML's learning and forecasting powers to diagnose and treat several diseases.…”

Section: Introductionmentioning

confidence: 99%

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A novel machine learning based hybrid approach for breast cancer relapse prediction

Sahoo,

Kumar Nayak,

Kumar Tripathy

et al. 2023

IJEECS

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<span>The second leading cause of death for women is breast cancer, which is growing. Some cancer cells may remain in the body, so relapse is possible even if treatment begins soon after diagnosis. Since there are now many machine learning (ML) approaches to recurrence prediction in breast cancer, it is important to compare and contrast them to find the most effective one. Datasets with many features often lead to incorrect predictions because of this. In this study, correlation-based feature selection (CFS) and the flower pollination algorithm (FPA) are used to improve the quality of the wisconsin prognostic breast cancer (WPBC) and University Medical Centre, Institute of Oncology (UMCIO) breast cancer relapse datasets respectively. Data imputation, scaling, pre-process raw data. The second stage uses CFS to select discriminative features based on important feature correlations. The FPA chose the optimum attribute combination for the most precise answer. We tested the approach using 10-fold cross-validation stratification. Various trials show 84.85% and 83.92% accuracy on the WPBC and UMCIO breast cancer relapse datasets, respectively. The hybrid method performed well in feature selection, increasing the accuracy of the relapse classification for breast cancer.</span>

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Section: Flower Pollination Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%