Reusing the code from Exploration Phase. The code and comments will be updated as proceeded. The code and comments might not be perfect until finalized # PCA section, Correlation, Heatmaps, can be seen to be the most important parts # Might have to adjust/improve my conclusions from the plots in future work # Target variable ACR i.e. Albumin Creatinine Ratio # Other Probable targets: Kidney failed, Systolic/Diastolic Pressure # Tried the following Target variables previously that might…

Reusing the code from Exploration Phase. The code and comments will be updated as proceeded. The code and comments might not be perfect until finalized # PCA section, Correlation, Heatmaps, can be seen to be the most important parts # Might have to adjust/improve my conclusions from the plots in future work # Target variable ACR i.e. Albumin Creatinine Ratio # Other Probable targets: Kidney failed, Systolic/Diastolic Pressure # Tried the following Target variables previously that might…

from __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline # code from xport library example or from ipwidget code examples - configuration code # for geo maps # !conda install basemap import conda import…

import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import warnings warnings.filterwarnings('ignore') pd.set_option('display.max_rows', 20) pd.set_option('display.max_columns', 500) # pd.set_option('display.width', 1000) data_folder = './data-for-code/' # import the CSV as a pandas dataframe df = pd.read_csv( data_folder + 'food_subgroup_intake_and_average_survival.csv') # show the first five rows df.shape df.head(5)…

The code shows the relation between food groups and CKD mortality Food Group concept is used as are used by CDC/USDA/USRDS Dietray Guidelines are reviewed every couple of years and published https://health.gov/our-work/food-nutrition/previous-dietary-guidelines/2015 A background database was there where some data pre-processing and adjustments were made Food Groups Survey Data and…

The code shows the relation between food sub groups and CKD mortality Food Sub Group concept is used as are used by CDC/USDA/USRDS Dietray Guidelines are reviewed every couple of years and published https://health.gov/our-work/food-nutrition/previous-dietary-guidelines/2015 A background database was there where some data pre-processing and adjustments were made Food Groups/Subgroups Survey…

Explore the CKD Patient Survival Data such as 90 day survival Related Datasets: Check some of the output from code to get an idea on how the dataset looks like I will utilize multiple related datasets All chapters of the 2018 USRDS ADR are accessible at this webpage: https://www.usrds.org/2018/view/Default.aspx Primary Dataset: Patient Characteristics i.e diagnostic results for Kidney/CKD/Renal patients https://www.usrds.org/2018/ref/ESRD_Ref_C_PatientChars_2018.xlsx From: https://www.usrds.org/reference.aspx

xport module need to be installed. from __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets import os out_folder = './nhanes_output_data/' data_folder = './nhanes_input_data/' diet_files = os.listdir(data_folder) diet_files[:4] ['2015-2016-food-codes-DRXFCD_I.XPT', '2015-2016-support-food-codes-DRXFCD_I.XPT'] import xport #import pandas as pd

from __future__ import print_function from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets import os data_folder = './csvdietfiles/' diet_files = os.listdir(data_folder) diet_files ['dairy_intakes.txt_Female.csv', 'dairy_intakes.txt_Male.csv', 'dark_green_vegetables_subgroup.txt_Female.csv', 'dark_green_vegetables_subgroup.txt_Male.csv', 'fruits_intake.txt_Female.csv', 'fruits_intake.txt_Male.csv', 'grains_intake.txt_Female.csv', 'grains_intake.txt_Male.csv', 'legumes_beans_and_peas_subgroup.txt_Female.csv', 'legumes_beans_and_peas_subgroup.txt_Male.csv', 'meat_poultry_and_eggs_subgroup.txt_Female.csv', 'meat_poultry_and_eggs_subgroup.txt_Male.csv', 'nuts_seeds_and_soy_products_subgroup.txt_Female.csv', 'nuts_seeds_and_soy_products_subgroup.txt_Male.csv', 'other_vegetables_subgroup.txt_Female.csv', 'other_vegetables_subgroup.txt_Male.csv', 'protein_intake.txt_Female.csv', 'protein_intake.txt_Male.csv', 'red_and_orange_vegetables_subgroup.txt_Female.csv', 'red_and_orange_vegetables_subgroup.txt_Male.csv', 'seafood_subgroup.txt_Female.csv', 'seafood_subgroup.txt_Male.csv', 'starchy_vegetables_subgroup.txt_Female.csv', 'starchy_vegetables_subgroup.txt_Male.csv', 'vegetable_intake.txt_Female.csv', 'vegetable_intake.txt_Male.csv']…