Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk

Shanbehzadeh, Mostafa; Nopour, Raoof; Kazemi-Arpanahi, Hadi

doi:10.30699/jambs.29.133.100

Cited by 14 publications

(14 citation statements)

References 36 publications

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“…In response to the above-mentioned challenges, healthcare systems across the world attempt to leverage machine learning (ML) classifiers for achieving proper decision-making via eliminating physicians’ subjective evaluations [ 11 , 18 , 19 ]. ML as a branch of artificial intelligence (AI) enables extracting high-quality predictive models from the mining of huge raw datasets [ 20 ]. It is a valuable tool that is even more employed in medical research to improve predictive modeling and reveal new contributing factors of a specific target outcome [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing machine learning algorithms for predicting COVID-19 mortality

Moulaei

Shanbehzadeh

Mohammadi-Taghiabad

et al. 2022

BMC Med Inform Decis Mak

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Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient’s data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. Methods In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models’ performance, the metrics derived from the confusion matrix were calculated. Results The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18–100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. Conclusion It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

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Section: Introductionmentioning

confidence: 99%

Comparing machine learning algorithms for predicting COVID-19 mortality

Moulaei

Shanbehzadeh

Mohammadi-Taghiabad

et al. 2022

BMC Med Inform Decis Mak

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“…C5.0, on the other hand, had the finest sensitivity result. Mostafa et al [14] examined the results of different classification algorithms for identifying Colorectal Cancer (CRC), namely, J-48, Bayesian NN, RF, and MLP. All methods were found to be suitable and capable of providing reasonable results.…”

Section: Related Research Workmentioning

confidence: 99%

“…It was found that all the three algorithms were performing well but kNN outperforms the other two by a difference of around 0.4 percent in accuracy. ROC [11], [14], [15], [18] 4 AUC [11], [13], [15], [18] 4 F-Measure [13], [14], [18], [19] 4…”

Section: Related Research Workmentioning

confidence: 99%

Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data

Thareja¹,

Chhillar²

2021

IJACSA

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“…In this situation, as overwhelmed health systems endeavor to escalate resource utilization and eliminate bottlenecks of patient hospitalization, adopting data-driven machine learning (ML) solutions will be useful (17). ML is a branch of arti cial intelligence (AI) that can apply to analysis and inference in a large volume of retrospective datasets to extract distinctive associations or make known unfamiliar patterns with minimal human intervention or any programming design (18,19). Evermore, ML techniques can employ in medical practice to increase prognostic modeling and reveal new contributing factors of a speci c target outcome to predict future or obscure trends (20,21).…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

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BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

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Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk

Cited by 14 publications

References 36 publications

Comparing machine learning algorithms for predicting COVID-19 mortality

Comparing machine learning algorithms for predicting COVID-19 mortality

Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

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