Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification

Xiang, Tian; Lim, Joon S.

doi:10.3233/bme-151495

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Only the variables with an average score of 3.75 (70%) or higher were allowed into the study. Finally, a total of 54 (22,23). From the group of functional classifiers, Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) are used.…”

Section: Study Predictorsmentioning

confidence: 99%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

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Section: Study Predictorsmentioning

confidence: 99%

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Shanbehzadeh¹,

Orooji²,

Kazemi-Arpanahi³

2021

jbe

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Genetic interactions effects for cancer disease identification using computational models: a review

Manavalan

Priya

2021

Med Biol Eng Comput

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Early adolescent brain markers of late adolescent academic functioning

Meruelo

Jacobus

Idy

et al. 2018

Brain Imaging and Behavior

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Academic performance in adolescence strongly influences adult prospects. Intelligence quotient (IQ) has historically been considered a strong predictor of academic performance. Less objectively explored have been morphometric features. We analyzed brain MRI morphometry metrics in early adolescence (age 12-14 years) as quantitative predictors of academic performance over high school using a naïve Bayesian classifier approach with n = 170 subjects. Based on the mean GPA, subjects were divided into high (GPA ≥3.54; n = 87) and low (GPA <3.54; n = 83) academic performers. Covariance analysis was performed to look at the influence of subject demographics. We examined predictive features from the 343 available regions (surface areas, cortical thickness, and subcortical volumes) and applied 4 algorithms for selection and reduction of attributes using Weka. Cortical thickness measures performed better than surface areas or subcortical volumes as predictors of academic performance. We identified 15 cortical thickness regions most predictive of academic performance, three of which have not been described in the literature predictive of academic performance. These were in the left hemisphere fusiform, bilateral insula, and left hemisphere paracentral regions. Prediction had a sensitivity of 0.65 and specificity of 0.73 with independent validation. Follow-up independent t-test analyses between high and low academic achievers on 10 of 15 regions showed between-group significance at the p < 0.05 level. High achievers demonstrated thicker cortices than low achievers. These newly identified regions may help pinpoint new targets for further study in understanding the developing adolescent brain in the classroom setting.

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Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification

Cited by 3 publications

References 27 publications

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

Genetic interactions effects for cancer disease identification using computational models: a review

Early adolescent brain markers of late adolescent academic functioning

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