Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

Tapak, Leili; Shirmohammadi‐Khorram, Nasrin; Amini, Payam; Alafchi, Behnaz; Hamidi, Omid; Poorolajal, Jalal

doi:10.1016/j.cegh.2018.10.003

Cited by 91 publications

(81 citation statements)

References 23 publications

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“…We also used the Waikato Environment for Knowledge Analysis (WEKA) package [25,26] to implement the three QC techniques and evaluated them through 10-fold cross-validation. Because the performance of 10-fold crossvalidation is generally checked with the average value [27][28][29][30][31], we showed the average RMSE for 10 folds. e parameters applied to each machine learning technique are as follows.…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Kim

Park²,

Choi³

et al. 2020

Computational Intelligence and Neuroscience

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We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Kim

Park²,

Choi³

et al. 2020

Computational Intelligence and Neuroscience

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“…The algorithms can uncover underlying patterns, build models, and make predictions based on the best-fit models. 47 Once the model has been optimized, the algorithm can run behind a simple software interface in the clinic into which quantitative data from the examination can be entered. Within seconds of adding these data, the objective diagnosis of the patient would be made based on a statistically powered algorithm.…”

Section: Discussionmentioning

confidence: 99%

Developing a periodontal disease antibody array for the prediction of severe periodontal disease using machine learning classifiers

Huang

Mao

et al. 2019

Journal of Periodontology

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Background The aim of this study was to simultaneously and quantitatively assess the expression levels of 20 periodontal disease‐related proteins in gingival crevicular fluid (GCF) from normal controls (NOR) and severe periodontitis (SP) patients with an antibody array. Methods Antibodies against 20 periodontal disease‐related proteins were spotted onto a glass slide to create a periodontal disease antibody array (PDD). The array was then incubated with GCF samples collected from 25 NOR and 25 SP patients. Differentially expressed proteins between NOR and SP patients were then used to build receiver operator characteristic (ROC) curves and compare five classification models, including support vector machine, random forest, k nearest neighbor, linear discriminant analysis, and Classification and Regression Trees. Results Seven proteins (C‐reactive protein, interleukin [IL]‐1α, interleukin‐1β, interleukin‐8, matrix metalloproteinase‐13, osteoprotegerin, and osteoactivin) were significantly upregulated in SP patients compared with NOR, while receptor activator of nuclear factor‐kappa was significantly downregulated. The highest diagnostic accuracy using a ROC curve was observed for IL‐1β with an area under the curve of 0.984. Five of the proteins (IL‐1β, IL‐8, MMP‐13, osteoprotegerin, and osteoactivin) were identified as important features for classification. Linear discriminant analysis had the highest classification accuracy across the five classification models that were tested. Conclusion This study highlights the potential of antibody arrays to diagnose periodontal disease.

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“…At the nal step, the subject will be assigned to the category with the highest posterior probability [21]. Naïve Bayes provides quality estimate of the attributes to conclude about the signi cant factors affecting measles [22].…”

Section: Naïve Bayesmentioning

confidence: 99%

Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran; 1997-2020

Nazari¹,

Fathi²,

Sharahi³

et al. 2020

Preprint

Self Cite

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Background: Measles is a feverish condition labeled among the most infectious viral illnesses in the globe. Despite the presence of a secure, accessible, affordable and efficient vaccine, measles continues to be a worldwide concern. Methods: This study uses machine learning and time series methods to assess factors that placed people at a higher risk of measles. This historical cohort study contained the Measles incidence in Markazi Province, the center of Iran, from April 1997 to February 2020. Logistic regression, linear discriminant analysis, random forest, artificial neural network, bagging, support vector machine, and naïve Bayes were used to make the classification. Zero-inflated negative binomial regression for time series was utilized to assess development of measles over time. Results: The prevalence of measles was 14.5% over the recent 24 years and a constant trend of almost zero cases was observed from 2002 to 2020. The order of independent variable importance were recent years, age, vaccination, rhinorrhea, male sex, contact with measles patients, cough, conjunctivitis, ethnic, and fever. Younger age, less probability of contact and no fever is associated with less odds of zero cases. Only 7 new cases were forecasted for the next two years. Bagging and random forest were the most accurate classification methods. Conclusion: Even if the numbers of new cases are almost zero during the recent years, it has been showed that age and contact are responsible for non-occurrence of measles. October and May are prone to have new cases for 2021 and 2022.

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Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

Cited by 91 publications

References 23 publications

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Developing a periodontal disease antibody array for the prediction of severe periodontal disease using machine learning classifiers

Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran; 1997-2020

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