Breast cancer prediction using an optimal machine learning technique for next generation sequences

Kurian, Babymol; Jyothi, V. L.

doi:10.1177/1063293x21991808

Cited by 21 publications

(7 citation statements)

References 28 publications

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“…The result of other studies confirm the better performance of DT than other similar algorithms in predicting the BC risk (44,45,50,51). Similarly, in this research, different ML algorithms were used to classify the data, and the DT algorithm had a higher efficiency than other algorithms.…”

Section: Discussionsupporting

confidence: 77%

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

et al. 2022

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Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers.

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Section: Discussionsupporting

confidence: 77%

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

et al. 2022

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“…While classifying with 12 features, the random forest algorithm achieved a classification accuracy of 74.73% and XGBoost achieved 73.63%. Nine supervised machine learning techniques including boosting algorithms were applied for breast cancer prediction by extracting 10 features from the genetic sequences of Homo sapiens, BRCA1, and BRCA2 [7]. e decision tree algorithm outperformed other models with 94.03% accuracy.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences

Kurian

Jyothi²

2022

Journal of Environmental and Public Health

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Breast cancer is the leading cancer in women, which accounts for millions of deaths worldwide. Early and accurate detection, prognosis, cure, and prevention of breast cancer is a major challenge to society. Hence, a precise and reliable system is vital for the classification of cancerous sequences. Machine learning classifiers contribute much to the process of early prediction and diagnosis of cancer. In this paper, a comparative study of four machine learning classifiers such as random forest, decision tree, AdaBoost, and gradient boosting is implemented for the classification of a benign and malignant tumor. To derive the most efficient machine learning model, NCBI datasets are utilized. Performance evaluation is conducted, and all four classifiers are compared based on the results. The aim of the work is to derive the most efficient machine-learning model for the diagnosis of breast cancer. It was observed that gradient boosting outperformed all other models and achieved a classification accuracy of 95.82%.

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“…They also observed that the accuracies has been increased a lot and has greater potential in classification of signals by frequency domain signals than the time domain signal. Detection of cancer was highly appreciated in the medical filed using Next Generation Sequencing application (Kurian and Jyothi, 2021). In (Vijayakumar et al, 2021), and the author contributes a precision classification technique to classify the breast cancer images using Deep Neural Network.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

Sabarivani

Ramadevi

2022

Concurrent Engineering

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In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.

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Breast cancer prediction using an optimal machine learning technique for next generation sequences

Cited by 21 publications

References 28 publications

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

[Retracted] Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

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