Chronic Kidney Disease Stage Identification in HIV Infected Patients using Machine Learning
Chronic Kidney Disease (CKD) is one of worldwide medical challenges with high morbidity and death rate. Since there is no symptom during the early stages of CKD, patients often fail to diagnose the disease. Patients with HIV have more chances to be affected with CKD in critical condition. Early detection of CKD helps patients to obtain prompt care ald delays the further progression of disease. With the availability of pathology data, the use of machine-learning techniques in healthcare for classification and prediction of disease has become more common. This paper presents the classification of CKD using machine learning models. Based on the glomerular filtration rate, the CKD stages are also calculated for patients diagnosed with CKD. DNN model outperforms with 99% of accuracy in classifying CKD patients with HIV.
ALGORITHM / MODEL USED:
Support Vector Machine (SVM)
PROJECT OUTPUT VIDEO:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python
- Web Framework : Flask
Prof. Asfakahemad Y Darveshwala, Prof. DheerajKumar Singh, Prof. Yassir Farooqui, “Chronic Kidney Disease Stage Identification in HIV Infected Patients using Machine Learning”, IEEE Conference, 2021.
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