Forecasting Diabetes Correlated Non-alcoholic Fatty Liver Disease by Exploiting Naïve Bayes Tree

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-29 DOI:10.4108/eai.29-4-2022.173975
S. Reddy, Nilambar Sethi, R. Rajender, G. Mahesh
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引用次数: 2

Abstract

INTRODUCTION: In recent years, non-alcoholic fatty liver disease (NAFLD) has been identified as the most vulnerable chronic disease. Fat is accumulated in the liver cells of persons with NAFLD. Diabetes is the most common ailment among people of all ages, so it is critical to recognize and prevent its adverse effects. OBJECTIVES: A relevant dataset with appropriate features was selected. Ensemble algorithms were applied for the prediction task, and finally, the method with the best performance was extracted. METHODS: In addition to Ensemble approaches namely bagging, Random forest and Ada-boost, individual classifiers Naive Bayes (NB) and C4.5 Decision tree were considered. These ML techniques were compared with the proposed NB tree algorithm, a combination of C4.5 and Naive Bayes. RESULTS: The following evaluation parameters were computed for each analyzed algorithm: accuracy, detection rate, negative predictive value (NPV), false negative rate (FNR), and false positive rate (FPR). The algorithms are then compared based on these metrics to determine the best algorithm. The NB tree was obtained to be the best method with 97.55% accuracy, 0.4853 detection rate, 0.9615 NPV, 0.0388 FNR, and 0.0099 FPR. CONCLUSION: The NB tree outperformed individual Naive bayes and C4.5 classifiers, and the other techniques studied. The developed algorithm could be applied in NAFLD-related research. accuracy, detection rate, NPV, FNR and FPR, diabetes mellitus (DM).
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利用Naïve贝叶斯树预测糖尿病相关非酒精性脂肪肝
近年来,非酒精性脂肪性肝病(NAFLD)已被确定为最易感的慢性疾病。脂肪在NAFLD患者的肝细胞中积累。糖尿病是所有年龄段人群中最常见的疾病,因此认识和预防其不良影响至关重要。目的:选择一个具有适当特征的相关数据集。将集成算法应用于预测任务,最终提取出性能最佳的方法。方法:除了综合方法即bagging、Random forest和Ada-boost外,还考虑了个体分类器朴素贝叶斯(NB)和C4.5决策树。将这些ML技术与提出的NB树算法(C4.5和朴素贝叶斯的结合)进行比较。结果:计算各分析算法的评价参数:准确率、检出率、阴性预测值(NPV)、假阴性率(FNR)、假阳性率(FPR)。然后根据这些指标对算法进行比较,以确定最佳算法。结果表明,NB树的准确率为97.55%,检出率为0.4853,NPV为0.9615,FNR为0.0388,FPR为0.0099。结论:NB树优于单个朴素贝叶斯和C4.5分类器,以及其他研究过的技术。该算法可应用于nafld相关研究。准确率,检出率,NPV, FNR和FPR,糖尿病(DM)。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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