Evaluation prediction techniques to achieve optimal biomedical analysis

Al-Janabi, Samaher; Mahdi, Muhammed Abaid

doi:10.1504/ijguc.2019.10020511

Cited by 13 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data mining algorithms are classified into two functional types, predictive and descriptive [5], and eight application types, classification, estimation, prediction, correlation analysis, sequence, time sequence, description, and visualization [6]. The successful application of data mining in biomedical research provides reliable support for clinical decision-making (e.g., disease diagnosis, therapy selection, and disease prognosis prediction) and management decision-making (e.g., staffing, medical insurance, and quality control) [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Zhao

Cui

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could predict IHD during pheochromocytoma surgery by data mining. Method: Relief-F was used to select the most important features. The accuracies of seven data mining models (CART, C4.5, C5.0, and C5.0 boosted), random forest algorithm, Naive Bayes and logistic regression were compared, the cross-validation, hold-out, and bootstrap methods were used in the validation phase. The accuracy of these models was calculated independently by dividing the training and the test sets. Receiver-Operating Characteristic curves were used to obtain the area under curve (AUC). Result: Random forest had the highest AUC and accuracy values of 0.8636 and 0.8509, respectively. Then, we improved the random forest algorithm according to the classification of imbalanced data. Improved random forest model had the highest specificity and precision among all algorithms, including relatively higher sensitivity (recall) and the highest f1-score integrating recall and precision. The important attributes were body mass index, mean age, 24 h urine vanillylmandelic acid/upper normal limit value, tumor size and enhanced computed tomography difference. Conclusions: The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases.

show abstract