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Kholida Hanum, Kholida
Teknik Elektro, Universitas Negeri Semarang, Kampus Sekaran Gunungpati, Semarang

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The Level of Asthma Diagnosing System by using Fuzzy Inference System Hanum, Kholida; Subiyanto, Subiyanto
Jurnal Ners Vol 10, No 1 (2015): Vol. 10 Nomor 1 April 2015
Publisher : Universitas Airlangga

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1000.778 KB)

Abstract

Introduction: This paper discuss about fuzzy inference system for the diagnosis of asthma’s levels. The process of diagnosis was made from symptoms that occur in patients with asthma. Input process, results, and methodology in making this system was done carefully, so this system is expected valid and fi t for medical diagnosis. Method: Methodology in the system including the knowledge base, fuzzyfi er, and inference engine. The symptoms used in diagnostic systems, including shortness of breath, wheezing, level of alertness/unique symptoms, respiratory rate, speech rate, pulse per minutes, and PEF after bronchodilator. And the output of asthma’s level diagnosis was mild, moderate, severe, and RAI/respiratory failure. The performance of system has been tested in Cilacap Pertamina Hospital, 20 patients with asthma were involved. The results of system and doctor’s opinion who has been treating patients with asthma were compared. Result: The result showed that the system obtained 90%, according to the doctor’s diagnosis. Discussion: This system is expected to help the medical expert or doctor in diagnosing the level of asthma. Keywords: Fuzzy inference system, diagnosis, level of asthma
The Level of Asthma Diagnosing System by using Fuzzy Inference System Hanum, Kholida; Subiyanto, Subiyanto
Jurnal NERS Vol 10, No 1 (2015): Vol. 10 Nomor 1 April 2015
Publisher : Universitas Airlangga

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1000.778 KB)

Abstract

Introduction: This paper discuss about fuzzy inference system for the diagnosis of asthma’s levels. The process of diagnosis was made from symptoms that occur in patients with asthma. Input process, results, and methodology in making this system was done carefully, so this system is expected valid and fi t for medical diagnosis. Method: Methodology in the system including the knowledge base, fuzzyfi er, and inference engine. The symptoms used in diagnostic systems, including shortness of breath, wheezing, level of alertness/unique symptoms, respiratory rate, speech rate, pulse per minutes, and PEF after bronchodilator. And the output of asthma’s level diagnosis was mild, moderate, severe, and RAI/respiratory failure. The performance of system has been tested in Cilacap Pertamina Hospital, 20 patients with asthma were involved. The results of system and doctor’s opinion who has been treating patients with asthma were compared. Result: The result showed that the system obtained 90%, according to the doctor’s diagnosis. Discussion: This system is expected to help the medical expert or doctor in diagnosing the level of asthma. Keywords: Fuzzy inference system, diagnosis, level of asthma