自适应神经模糊与模糊推理系统在高血压诊断中的比较研究

Rimpy Nohria
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引用次数: 1

摘要

提出了一种用于高血压诊断的自适应神经模糊推理系统(ANFIS)。高血压患者的病历在医生的监督下实时收集。采用混合训练算法对系统进行训练。利用新患者病历对训练后的模型进行了检验。并将该系统的诊断性能与医生对相同病例的判断进行了比较。并在几个参数方面与现有的模糊专家系统进行了比较。从医生收集的记录实验中,我们获得了94.63%的准确率,对于现有的系统来说是非常有希望的。诊断高血压的灵敏度为97.50%,特异性为93.33%,精密度为98.11%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative study of adaptive neuro-fuzzy and fuzzy inference system for diagnosis of hypertension
Adaptive neuro-fuzzy inference system (ANFIS) for diagnosis of hypertension is proposed in this paper. Hypertension patient's records are collected under the supervision of physician on real time basis. The system is trained using hybrid training algorithm. The trained model is tested by using new patient's record. Further the performance of the diagnosing capability of the system is compared with judgment of physician on same test cases. The performance of the system is also compared with existing fuzzy expert system in terms of several parameters. We have obtained 94.63% accuracy from the experiments made on record collected from physician and it was very promising with regard to existing system. We have obtained sensitivity 97.50%, specificity 93.33% and precision 98.11% values for diagnosis hypertension.
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