DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Vivian Siahaan

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Chronic kidney disease is the longstanding disease of the kidneys leading to renal failure. The kidneys filter waste and excess fluid from the blood. As kidneys fail, waste builds up. Symptoms develop slowly and aren't specific to the disease. Some people have no symptoms at all and are diagnosed by a lab test. Medication helps manage symptoms. In later stages, filtering the blood with a machine or a transplant may be required

The dataset used in this project was taken over a 2-month period in India with 25 features . The target is the 'classification', which is either 'ckd' or 'notckd' - ckd=chronic kidney disease. It contains measures of 24 features for 400 people. Quite a lot of features for just 400 samples. There are 14 categorical features, while 10 are numerical. The dataset needs in that it has NaNs and the numeric features need to be forced to floats.

Attribute Age age in years; Blood Pressure bp in mm/Hg; Specific Gravity sg - ; Albumin al - ; Sugar su - ; Red Blood Cells rbc - ; Pus Cell pc - ; Pus Cell clumps pcc - ; Bacteria ba - ; Blood Glucose Random bgr in mgs/dl; Blood Urea bu in mgs/dl; Serum Creatinine sc in mgs/dl; Sodium sod in mEq/L; Potassium pot in mEq/L; Hemoglobin hemo in gms; Packed Cell Volume; White Blood Cell Count wc in cells/cumm; Red Blood Cell Count rc in millions/cmm; Hypertension htn - ; Diabetes Mellitus dm - ; Coronary Artery Disease cad - ; Appetite appet - ; Pedal Edema pe - ; Anemia ane - ; and Class class - .

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy.

Language: English

Pages: 218

Format: Paperback

Release: November 12, 2021

ISBN 13: 9798766167082

DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Vivian Siahaan

0/5 ( ratings)

Read Download

Language: English

Pages: 218

Format: Paperback

Release: November 12, 2021

ISBN 13: 9798766167082

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DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

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