Breast Cancer Prediction Using Spark MLlib and ML Packages

Hung, Phan Duy; Hanh, Tran Duc; Diep, Vu Thu

doi:10.1145/3309129.3309133

Cited by 25 publications

(9 citation statements)

References 12 publications

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“…Extreme learning machine [78] 80.00% PySpark and its machine learning frameworks [79] 83.0% Classification via regression [80] 80.0% Proposed model 86.97% Table A3) was 86.97%, as applied to the BCCD data set. Furthermore, a comparison of the results of six methods (Table II) using the same data set suggested that the proposed method is clearly superior.…”

Section: Resultsmentioning

confidence: 99%

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Chiu

Kuo

2020

IEEE Access

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Section: Resultsmentioning

confidence: 99%

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Chiu

Kuo

2020

IEEE Access

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“…The result of customer clustering can be used in marketing strategies, forecasting of affordability, or customer deposits in the following month, etc. The paper is also a valuable reference for problems in areas such as Knowledge Representation [28], [29], Prediction [30], Time Series Analysis [31], etc. It can also be applied when dealing with time series data such as biomedical signals, insurance expenditures, etc.…”

Section: Discussionmentioning

confidence: 99%

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Hung¹,

Dat²

2020

IJMLC

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Customer clustering, the division of customers into different groups, is a classical problem. It is especially important in banking as it serves multiple purposes in marketing, risk management, etc. Therefore, it has attracted the use of many modern machine learning models and techniques. But currently, most of them are only making use of "static" customer information. This paper proposes a new approach for customer clustering in banking based on the customers' balance history. Basic Dynamic Time Warping (DTW) distance and Soft-DTW (SDTW) distance, an advanced form, are used to measure the difference between customers. To which, the two most popular strategies in time series clustering strategies, partitional and hierarchical, are applied which. In additional, some statistical features are given to prove the effectiveness of the proposed method.

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“…VectorAssembler function that transform all columns, both raw and calculated, into a single vector column can be passed to the ML algorithm [20]. Furthermore, we identi ed factors that have the greatest importance on the prediction and signi cantly in uence the performance of the model.…”

Section: Methodsmentioning

confidence: 99%

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Daghistani¹,

AlGhamdi²,

Alshammari

et al. 2020

Preprint

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Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

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Breast Cancer Prediction Using Spark MLlib and ML Packages

Cited by 25 publications

References 12 publications

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

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