Parallel Implementation of kNN Algorithm for Breast Cancer Detection

Athani, Suhas; Joshi, Shreesha; Rao, B. Ashwath; Rai, Shwetha; Kini, Gopalakrishna N

doi:10.1007/978-981-15-5788-0_46

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…This approach classifies a sample by looking at its previously classified neighbor samples and is independent of the hidden joint distribution on other samples and their classification. The literature has different applications of kNN on cancer diagnosis, particularly in breast cancer [ 39 , 40 , 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Bostancı

Koçak

Ünal

et al. 2023

Sensors

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Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Bostancı

Koçak

Ünal

et al. 2023

Sensors

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“…KNN algorithm is a non-parametric classifier and simple ML technique. The KNN strategy focuses on the similarity between the new data/samples and available samples and puts the new samples into the group that is most analogous to the existing groups [64], [65]. The KNN strategy has been used for tumor classification in the BC field.…”

Section: K-nearest Neighbors Algorithmmentioning

confidence: 99%

“…According to the result of this study, KNN method had better performance in BC classification. Athani et al [64] predicted and classified BC using a KNN algorithm through parallel programming to decrease the procedure time in comparison with the sequential execution form.…”

Section: K-nearest Neighbors Algorithmmentioning

confidence: 99%

Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2023

Computers in Biology and Medicine

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“…Islam et al, 2019). The KNN algorithm is a machine learning technique that has non-parametric properties (Athani et al, 2021). The non-parametric approach suggests that it does not depend on any presumptions about how the underlying data will be distributed (Rogers et al, 2019).…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Machine Learning to Identify Monkey Pox Disease

Aldi

Nozomi

Sentosa

et al. 2023

SinkrOn

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In May 2022, it has received by WHO reports from non-endemic countries on cases of monkey pox disease. Monkey pox is a rare zoonotic disease caused by infection with the monkeypox virus that belongs to the genus orthopoxvirus and the family poxviridae, and also the variola virus. This study aims to classify patients who have contracted the monkey pox virus. We modeled an analysis of monkey pox disease and conducted comparisons utilizing a dataset from Kaggle consisting of a CSV file with records for 25,000 patients. The monkey pox dataset was analyzed using the correlation coefficient and the number of target variables. Machine learning (ML) methods are used for classification by utilizing the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) algorithms. This study resulted in the highest classifier Gradient Boosting (GB) algorithm with an accuracy value of 71%. then the accuracy obtained by Support Vector Machine (SVM) is 69%, Random Forest (RF) accuracy is 68%, and finally K-Nearest Neighbor (KNN) obtains 63% accuracy. This ML method is expected to analyze monkey pox disease so that it helps the country and government, especially the health field in assessing, identifying, and being able to take appropriate action against monkey pox disease.

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Parallel Implementation of kNN Algorithm for Breast Cancer Detection

Cited by 5 publications

References 11 publications

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

Machine Learning to Identify Monkey Pox Disease

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