A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases

Pérez, Joaquín; Iturbide, Emmanuel; Olivares, Víctor; Hidalgo, M. A.; Martínez, Alicia; Almanza, Nelva

doi:10.1007/s10916-015-0312-5

Cited by 18 publications

(8 citation statements)

References 10 publications

(10 reference statements)

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“…The original dataset will thus cause poor performance of the subsequent prediction model and will become a bottleneck in the process of data mining. Therefore, the process of data preprocessing, including data reduction, data cleaning, data transformation and data integration, is crucial [57] and typically comprises 70~80% of the workload of data mining [58].…”

Section: Discussionmentioning

confidence: 99%

Predicting breast cancer 5-year survival using machine learning: A systematic review

Zhou

Dong

et al. 2021

PLoS ONE

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Background Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Results Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Conclusions Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.

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Section: Discussionmentioning

confidence: 99%

Predicting breast cancer 5-year survival using machine learning: A systematic review

Zhou

Dong

et al. 2021

PLoS ONE

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“…This requires a good understanding of the data mining goals as well as the data itself (Pyle, Editor, & Cerra, 1999). Data selection, also called "Dimensionality Reduction" (Liu & Motoda, 1998), consists in vertical (attributes/variables) selection and horizontal (instance/records) selection (García, Luengo, & Herrera, 2015;Nisbet, Elder, & Miner, 2009;Pérez et al, 2015) (Table 6). Also, it is worth noticing that models obtained from a reduced number of features will be easier to understand (Pyle et al, 1999).…”

Section: Data Selectionmentioning

confidence: 99%

Predicting academic success in higher education: literature review and best practices

Alyahyan

Düştegör

2020

Int J Educ Technol High Educ

306

177

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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution's performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to "computer science", or more precisely, "artificial intelligence" literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student's success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

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“…Another focus of the researchers in this domain was to compare the performance of simulation platforms. For instance, in [18] In [19], a set of individual classifiers involved in an ensemble classifier, solo classifiers and neural network classifiers was applied on 4 datasets provided by UCI: the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the ILPD, the VCDS and the HDDS. Different from the similar studies, the focus of [20] was Fatty Liver Disease (FLD) and several methods such as Decision Tree, SVM, AdaBoost, KNN, Probabilistic Neural Network (PNN), Naive Bayes and Fuzzy Sugeno were used to work with normal and abnormal liver images through linear and quadratic discriminant analysis.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Daş

2020

Sakarya University Journal of Computer and Information Sciences

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In recent years, different approaches and methods have been proposed to diagnose various diseases accurately. Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend to be specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases. In this study, we compare the accuracy of different classification methods supported by the SAS software suite, such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), and HP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided by the University of California, Irvine (UCI) repository is used. Experimental results show that based on the metrics of our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree do the lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rate and HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers and practitioners working in related fields.

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A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases

Cited by 18 publications

References 10 publications

Predicting breast cancer 5-year survival using machine learning: A systematic review

Predicting breast cancer 5-year survival using machine learning: A systematic review

Predicting academic success in higher education: literature review and best practices

A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

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