PERANCANGAN SISTEM INFORMASI ANALISIS MEDIK MENGGUNAKAN LOGIKA FUZZY SUGENO BERBASIS DATA REKAM MEDIK PADA PENYAKIT HIPERTENSI
Abstract
Number of patients with hypertension have increased from year to year. This is due to an unhealthy lifestyle, excessive stress and so forth. Patiens with hypertension are often unaware of the dangers that can be caused by disease. The medical diagnosis of hypertension is made when obtained blood pressure is obtained more than 140/90 mmHg. In this study, created a system of enforcement of hypertension using sugeno fuzzy logic. This application uses the parameters of age, body mass index, blod pressure(systole and diastole), family history (genetics), diabetes mellitus. From testing the fuzzy logic that has been done, can be obtained on the calculation of 60 patients who did not suffer from hypertension (normal) as much 3 people, who suffer from prehypertension as much 17 people, who suffer from hypertension grade 1 as much 25 people and who suffer from hypertension degrees 2 as much 15 people. With an average accuracy test rate 99.999989% for patients who did not suffer from hypertension, 99.999985% for patients who suffer from prehypertension, 100% who suffer from hypertension grade 1 and 99.999946% who suffer from hypertension degrees 2. With an average the level of error 0.00333% for patients who did not suffer from hypertension, 0.00471% for patients who suffer from prehypertension, 0% who suffer from hypertension grade 1 and 0.00733% who suffer from hypertension degrees 2. From the application made can provide clear, accurate information about hypertension disease.
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References
Vitahealth. 2009.Hipertensi. PT. Gramedia Pustaka Utama,Jakarta.
Djam, X. Y., and Y. H. Kimbi.Fuzzy Expert System For The Management Of Hypertension. The Pacific Journal of Science and Technology 12, no. 1 (2011): 390-402
Abdullah, Azian Azamimi, Zulkarnay Zakaria, and Nur Farahiyah Mohamad.Design And Development Of Fuzzy Expert System For Diagnosis Of Hypertension.In Intelligent Systems, Modelling and Simulation (ISMS), 2011 Second International Conference on, pp. 113-117. IEEE, 2011
Kaur, Rupinder, and Amrit Kaur. Hypertension Diagnosis Using Fuzzy Expert System. In International Journal of Engineering Research and Applications (IJERA) National Conference on Advances in Engineering and Technology, AET-29th March. 2014.
Soelistijorini, Reni, Mike Yuliana, Ira Prasetyaningrum, and Lina Pratiwi. Implementation Of Medical Error Prevention System For Hypertension Disease Based On Fuzzy. In Science in Information Technology (ICSITech), 2016 2nd International Conference on, pp. 238-243. IEEE, 2016.
Das, Sujit, Pijush Kanti Ghosh, and Samarjit Kar. Hypertension Diagnosis: A Comparative Study Using Fuzzy Expert System And Neuro Fuzzy System. In Fuzzy Systems (FUZZ). 2013 IEEE International Conference on, pp. 1-7. IEEE, 2013.
Abrishami, Zeinab, and Hamid Tabatabaee. Design Of A Fuzzy Expert System And A Multi-Layer Neural Network System For Diagnosis Of Hypertension. Bull. Env. Pharmacol. Life Sci 4 (2015): 138-145.
Baihaqi, Wiga Maulana, Noor Akhmad Setiawan, and Igi Ardiyanto. Rule Extraction For Fuzzy Expert System To Diagnose Coronary Artery Disease. In Information Technology, Information Systems and Electrical Engineering (ICITISEE), International Conference on, pp. 136-141. IEEE, 2016.
Cunha, Vanessa S., Cátia M. Salgado, Susana M. Vieira, and João MC Sousa. Fuzzy Modeling To Predict Short And Long-Term Mortality Among Patients With Acute Kidney Injury. In Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on, pp. 148-153. IEEE, 2016.
Putra, Aditya Agung, and Rinaldi Munir. Implementation Of Fuzzy Inference System In Children Skin Disease Diagnosis Application.In Electrical Engineering and Informatics (ICEEI), 2015 International Conference on, pp. 365-370. IEEE, 2015.
Undre, Poonam, Harjeet Kaur, and Prakash Patil. Improvement In Prediction Rate And Accuracy Of Diabetic Diagnosis System Using Fuzzy Logic Hybrid Combination.In Pervasive Computing (ICPC), 2015 International Conference on, pp. 1-4. IEEE, 2015.
Oladele, T. O., J. S. Sadiku, and R. O. Oladele.Coactive Neuro-Fuzzy Expert System: A Framework For Diagnosis Of Malaria, African J. of Computing & ICT 7, no. 2 (2014): 174-188.
Badnjevic, Almir, Mario Cifrek, Dragan Koruga, and Dinko Osmankovic,Neuro-Fuzzy Classification Of Asthma And Chronic Obstructive Pulmonary Disease, BMC medical informatics and decision making 15, no. 3 (2015): S1.
Saritas, Ismail, Ilker A. Ozkan, Novruz Allahverdi, and Mustafa Argindogan.Determination Of The Drug Dose By Fuzzy Expert System In Treatment Of Chronic Intestine Inflammation. Journal of Intelligent Manufacturing 20, no. 2 (2009): 169-176.
Imran, Mohammed, Alhanouf M. Al-Abdullatif, Bushra S. Al-Awwad, Mzoon M. Alwalmani, Sarah A. Al-Suhaibani, and Shahad A. Al-Sayah. 2016.Towards Early Detection of Diabetic Retinopathy Using Extended Fuzzy Logic..
Lee, Byung-Kwan, Eun-Hee Jeong, and Sang-Sik Lee.Context-Awareness Healthcare for Disease Reasoning Based on Fuzzy Logic. Journal of Electrical Engineering and Technology 11, no. 1 (2016): 247-256.
Korenevskiy, Nikolay, Mahdi Alshamasin, Riad Taha Al-Kasasbeh, Krupchatnikov Roman Anatolevich, and Florin Ionescu.Prediction And Prenosological Diagnosis Of Stomach Diseases Based On Energy Characteristics Of Acupuncture Points And Fuzzy Logic. International Journal of Modelling, Identification and Control 23, no. 1 (2015): 55-67.
Ansari, A. Q., and Neeraj Kumar Gupta.Automated Diagnosis Of Coronary Heart Disease Using Neuro-Fuzzy Integrated System. In Information and Communication Technologies (WICT), 2011 World Congress on, pp. 1379-1384. IEEE, 2011.
Onuwa, OJEME BLESSING.Fuzzy Expert System For Malaria Diagnosis. Oriental J. of Computer Science and Technology 7 (2014): 273-284.
Williams, Samuel Oluwarotimi, and Omisore Mumini Olatunji.Genetic Neuro-Fuzzy System For The Diagnosis Of Typhoid Fever. In 2013 Conference on Medical Innovation and Computing Service. 2013.
James, T. R. Simulation Of Medical Diagnosis System For Malaria Using Fuzzy Logic. SIMULATION 5, no. 2 (2014).
Aru W. Sudoyo, dkk. 2007.Buku Ajar Ilmu Penyakit Dalam Jilid I edisi IV. Jakarta: Pusat Penerbitan Departemen Ilmu Penyakit Dalam Fakultas Kedokteran Universitas Indonesia.
Pressman, Roger S. 2002.Rekayasa Perangkat Lunak Pendekatan Praktisi (Buku 1). Edisi 2. Yogyakarta:Andi.
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