Prediction of Chronic Kidney Disease Using Deep Neural Network
| Autor: | Saidu Isah Rambo, Ali Baba Dauda, Iliyas Ibrahim Iliyas, Suleiman Tasiu |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning medicine.medical_specialty Creatinine Heart disease business.industry Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Disease Electrical Engineering and Systems Science - Image and Video Processing medicine.disease Machine Learning (cs.LG) chemistry.chemical_compound chemistry Diabetes mellitus medicine FOS: Electrical engineering electronic engineering information engineering General hospital Intensive care medicine business Kidney disease |
| DOI: | 10.48550/arxiv.2012.12089 |
| Popis: | Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidney disease. This disease is becoming prevalent due to substances and elements we intake. Death is imminent and inevitable within few days without at least one functioning kidney. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized lately. Bade, a Local Government of Yobe state in Nigeria has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 400 patients with 10 attributes as our dataset from Bade General Hospital. We used DNN model to predict the absence or presence of CKD in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to provide the ranking of the features used in the prediction of the CKD. The outcome revealed that two attributes; Creatinine and Bicarbonate have the highest influence on the CKD prediction. Comment: 14 paages |
| Databáze: | OpenAIRE |
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