Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm

Thottathyl, Hymavathi; Kanadam, Karteeka Pavan; Panchadula, Rajeev Priyatam

doi:10.18280/ria.340610

Cited by 6 publications

(3 citation statements)

References 17 publications

(26 reference statements)

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“…The quantitative K-means method described in this paper is especially important to sufficiently summarize baseline symptom data that oftentimes has high dimensionality due to many recorded symptoms/toxicities. K-means has proven its utility for large-scale data through analysis of Internet text data; it has also been used effectively in oncological research in microarray breast cancer data clustering [32,33].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials

Nicholls¹,

Xu²,

Kaneswaran³

et al. 2022

RIA

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Patients in randomized controlled trials (RCTs) must be successfully randomized to reduce or eliminate bias. Because pre-treatment symptoms have prognostic significance in cancer patients, qualitative and quantitative tools were developed to assess similarity of baseline pre-treatment symptoms across different treatment arms of RCTs as one measure of randomization success. Clinician-reported symptom data from two colorectal cancer RCTs, CO.20 and CO.17, were used to demonstrate the utility of a qualitative visualization tool and quantitative machine learning K-means tool, which grouped patients into clusters using baseline symptoms. Qualitatively, reflection bar graphs (RBGs) visualized potential imbalances in baseline symptoms (i) across treatment arms and (ii) by corresponding patient clusters identified within each treatment arm. RBGs found that the treatment arms for both RCTs had similar symptom profiles, while the lack of significant differences in the proportions of patients in each cluster across treatment arms further confirmed successful randomization. This paper details the creation of visualization, machine-learning, and statistical tools to compare baseline symptoms across RCT treatment arms, demonstrating that the CO.20 and CO.17 trials were successfully randomized by baseline symptoms and are comparable. These tools can therefore be implemented easily to ensure an extra layer of quality assurance of the randomization process for study assessment.

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Section: Discussionmentioning

confidence: 99%

Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials

Nicholls¹,

Xu²,

Kaneswaran³

et al. 2022

RIA

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“…Before proceeding, the data is first clustered to be able to better mine the useful value information of employment education data (Thottathyl et al 2020). Clustering analysis is a notable excavation of data excavation, and as one of the classical algorithms, the K-means is scalable, simple in principle, uncomplicated to exert, and has obvious behaviour advantages in data integration.…”

Section: Data Mining-based Sustainable Education Management For Emplo...mentioning

confidence: 99%

A Study on the Application of Data Mining Techniques in the Management of Sustainable Education for Employment

方放¹

2023

Data Science Journal

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With the gradual advancement of education management towards data and informationisation, how to establish a perfect employment education management system has become an important element of current student work. Data mining technology can extract valuable implicit information from the database, and is widely used in the practical processing of massive data. Based on the analysis of the characteristics of employment education management in universities, the study first improved the K-means algorithm by adding splitting and aggregation operations to it, used the improved K-means algorithm to cluster and analyse the employment education data, and then combined it with the optimised Apriori algorithm to further mine the useful information in the employment education data. The experimental outcomes demonstrate that the error rate of the improved K-means algorithm is stable at around 10%, with a high accuracy rate and strong stability; combined with the optimised Apriori algorithm applied to the employment education management system, the accuracy rate is basically maintained at over 96%, and the scores of students' employment knowledge and employment practice are all over 90, indicating that the method can provide effective guidance for students' employment and give a guideline for the sustainable education management of employment.

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“…In their research, Thottathyl et al [25] implemented a K-means clustering algorithm on the Wisconsin dataset for early detection of breast cancer.…”

Section: 3body Characteristicmentioning

confidence: 99%

Machine learning techniques with ANOVA for the prediction of breast cancer

2022

IJATEE

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Breast cancer is one of the most common cancer among females. In this paper, machine learning techniques are applied to molecular taxonomy of breast cancer international consortium (METABRIC) dataset to extract prime clinical attributes to get high accuracy. Analysis of variance (ANOVA), the statistical method, is used for clinical feature selection. Five different machine learning algorithms are implemented, which are support vector machine (SVM), decision tree, random forest, AdaBoost and artificial neural network (ANN). Among all the machine learning classifiers, ANN gives the highest accuracy of 87.43%. This statistical technique is helpful for the detection of breast cancer, and it will increase the survival rate of females.

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Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm

Cited by 6 publications

References 17 publications

Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials

Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials

A Study on the Application of Data Mining Techniques in the Management of Sustainable Education for Employment

Machine learning techniques with ANOVA for the prediction of breast cancer

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