https://github.com/kmohamedalie/chronic-kidney-disease-detection

Dectecting chronic heart disease using machine learning 100%
https://github.com/kmohamedalie/chronic-kidney-disease-detection

kidney-disease machine-learning-algorithms medical-application snapml suppoert-vector-machine

Last synced: 3 months ago
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Dectecting chronic heart disease using machine learning 100%

• Host: GitHub
• URL: https://github.com/kmohamedalie/chronic-kidney-disease-detection
• Owner: Kmohamedalie
• License: mit
• Created: 2023-08-16T23:25:09.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2023-08-22T14:15:51.000Z (almost 2 years ago)
• Last Synced: 2025-01-02T15:32:01.886Z (5 months ago)
• Topics: kidney-disease, machine-learning-algorithms, medical-application, snapml, suppoert-vector-machine
• Language: Jupyter Notebook
• Homepage: https://github.com/Kmohamedalie/Chronic-Kidney-Disease
• Size: 194 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Chronic Kidney Disease Detection

https://github.com/Kmohamedalie/Chronic-Kidney-Disease/assets/63104472/071f080b-46af-48f0-82c9-b8835e26db3f

**Source:** [Centers for Disease Control](https://www.youtube.com/watch?v=FdxGclFztC0)

**more youtbe videos:** [NHS Imperial College Health](https://www.youtube.com/watch?v=tyWgdBYBttc)

**Task:** Detect chronic kidney disease using support vector machine, the class is binary with the value (ckd,notckd). Where cdk means the presence of Chronic Kidney disease and not 'cdk' means healthy.

**Dataset:**  UCI Machine Learning

**Complete JupyterNotebook:** [Link](https://github.com/Kmohamedalie/Chronic-Kidney-Disease/blob/master/Notebook/Chronic%20KIdney%20Disease%20svm-%20SnapML.ipynb)

| Algorithm | Recall | Precision | F1-score | Accuracy |Jaccard Index |

| --------- |--------|-----------|----------|----------|----------|

|Support Vector Machine (linear) |  100%  |    100% |   100% |  100%  |100%  |

    




### **Additional Information about the dataset**

This dataset is composed of 400 instances with 25 attributes, it contains missing values as welll make almost 55%, so please make sure to use EDA before running any visualization of ML algorithm.




### **Attributes**

We use the following representation to collect the dataset

            age		-	age	              


			bp		-	blood pressure    


			sg		-	specific gravity  


			al		-   	albumin       


			su		-	sugar             
            

			rbc		-	red blood cells   


			pc		-	pus cell          


			pcc		-	pus cell clumps   


			ba		-	bacteria          


			bgr		-	blood glucose random   


			bu		-	blood urea             


			sc		-	serum creatinine       


			sod		-	sodium                 


			pot		-	potassium              


			hemo		-	hemoglobin         


			pcv		-	packed cell volume     


			wc		-	white blood cell count 


			rc		-	red blood cell count   


			htn		-	hypertension           


			dm		-	diabetes mellitus      


			cad		-	coronary artery disease 


			appet		-	appetite            


			pe		-	pedal edema             


			ane		-	anemia                   


			class - (ckd,notckd)