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Developing diabetes ketoacidosis prediction using ANFIS model

机译：使用ANFIS模型开发糖尿病酮症酸中毒预测

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This paper proposes the adaptive Neuro-fuzzy Inference System (ANFIS) to construct a diabetic ketoacidosis prediction model. Diabetic ketoacidosis results in large amount of ketones can be detected in urine through distinctive odour of acetone. Hence, urine odour analysis is able to diagnose diabetic ketoacidosis facilitating dianogstic test for diabetic patient. The Electronic Nose (E-nose) system consists of four metal oxide gas sensors was used to extract urine odour. Common process of diabetic diagnosis require patient to provide fasting urine for more accurate detection. Our work has shown both fasting and non-fasting urine are able to produce good diabetic detection accuracy through urine odour analysis. A total of 40 human subjects from CITO Laboratory, Semarang Central Java, Indonesia, involving 20 diabetic patients and 20 healthy subjects were used to build the prediction model. The proposed model has achieved at least 63% average accuracy in discriminating diabetic patient from healthy subject. When data preprocessing to average the training data samples was implemented, the detection performance was increased to above 93% The findings have shown promising results of using both fasting and non-fasting data samples for diabetic prediction. This is essential towards the flexibility of diabetic dianogstic test where it does not require patient to fast, therefore can be tested at anytime anywhere.
机译：本文提出了适应性神经模糊推理系统（ANFIS）构建糖尿病酮症化预测模型。通过丙酮的独特气味，可以在尿液中检测到大量酮的糖尿病酮症期。因此，尿气味分析能够诊断促进糖尿病患者的衰尖试验的糖尿病酮蚴病。电子鼻子（E-鼻子）系统由四种金属氧化物气体传感器组成，用于提取尿液气味。糖尿病诊断的常见过程需要患者提供禁食尿液以进行更准确的检测。我们的工作表明，禁食和非禁食尿液能够通过尿气味分析产生良好的糖尿病检测精度。共有40名来自Cito实验室，Semarang Central Java，印度尼西亚的人类受试者，涉及20名糖尿病患者和20名健康受试者来构建预测模型。该模型在鉴别健康受试者鉴别糖尿病患者的情况下实现了至少63％的平均精度。当实施预处理到平均训练数据样本的数据时，检测性能增加到93以上，结果显示了使用禁食预测的禁食和非空腹数据样本的有希望的结果。这对于糖尿病患者测试的灵活性至关重要，其中它不需要患者快速，因此可以随时随地进行测试。

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