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Title |
Intelligent Identification of Acute Kidney Injury empowered with Heterogeneous Mamdani Fuzzy Inference System |
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Author |
Shahzada Atif Naveed, Syed Anwar Hussnain, Asma Baloch, Muhammad Adnan Khan, Areej Fatima, Atifa Athar, Muhammad Asif |
| Citation |
Vol. 25 No. 12 pp. 134-140 |
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Abstract |
In this article, a new Heterogeneous-Layered Mamdani Fuzzy Inference System (HL-MFIS) is proposed to detect the Acute Kidney Injury. The proposed computerized system Detect of AKI Using Heterogeneous Mamdani Fuzzy Inference System (DAKI-HL-MFIS) Expert System, can detect the Acute Kidney Injury or No-AKI. The Expert System has two input variables at layer-I and seven input variables at layers-II. At layer-I input variables are Creatinine and BUN that detects the output condition of a Kidney to be Normal, or Acute Kidney Injury. The further input variables at layer-II are Glomerular filtration rate, urine Albumin, sodium, potassium, chloride, calcium and phosphorus that determine the output condition of Kidneys like Acute kidney Injury and other reasons that arise due to enzyme vaccination or due to past Kidney Injury. The overall accuracy of the DAKI-HL-MFIS Expert system is 90.5%. |
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Keywords |
MFIS, Creatinine, AKI, Albumin creatinine ratio, BUN, Sodium, Potassium, Chloride, Calcium, Phosphorus, Albumin, DAKI-HL-MFIS, ES |
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