HTN | HTML to Native like swift and objective-c | iOS library

df_nhpi %>%    
select(AGE, SEX, MAR_STAT, HEIGHT, WEIGHT, BMI, HTN, HTNMED, MI, Smoking, COPD, CANCER, DIABETES) %>%   
tbl_summary(by = SEX,               
           label = list(MAR_STAT ~ 'Marital Status',        
                        HTN ~ 'Hypertension',                            
                        HTNMED ~ 'Hypertension Medication',                            
                        MI ~ 'Heart Attack',                             
                        Smoking ~ 'Smoking Status',                             
                        COPD ~ 'Chronic Obstructive Pulmonary Disease'),               
           type = list(c("HTN","HTNMED", "MI", "COPD", "CANCER") ~ "categorical"),               
           missing = "ifany",               
           missing_text = "Unknown",               
           statistic = list(all_continuous() ~ "{mean} ({sd})",                                
                            all_categorical() ~ "{n} ({p}%)"),               
           digits = all_continuous() ~ 2, percent = "column") %>%   
add_stat_label() %>%   
add_p(test = all_continuous() ~ "t.test", pvalue_fun = 
           function(x) style_pvalue(x, digits = 3)) %>%   
bold_p() %>%   
modify_caption("**Table 1. Baseline Characteristics**") %>%   bold_labels()

import tensorflow as tf
from tensorflow import keras
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
xi=pd.read_csv(r'/content/ckd_full.csv')
xi=xi.drop(columns=['sg','su','pc','pcc','pcv','rbcc','wbcc'])
y=xi[['class']]
y['class']=y['class'].replace(to_replace=(r'ckd',r'notckd'), value=(1,0))
x=xi.drop(columns=['class'])


x['rbc']=x['rbc'].replace(to_replace=(r'normal',r'abnormal'), value=(1,0))
x['ba']=x['ba'].replace(to_replace=(r'present',r'notpresent'), value=(1,0))
x['htn']=x['htn'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['dm']=x['dm'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['cad']=x['cad'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['pe']=x['pe'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['ane']=x['ane'].replace(to_replace=(r'yes',r'no'), value=(1,0))
x['appet']=x['appet'].replace(to_replace=(r'good',r'poor'), value=(1,0))
x[x=="?"]=np.nan
d=['age', 'bp', 'al', 'rbc', 'ba', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'htn', 'dm','cad', 'appet', 'pe', 'ane']

for i in d:
  x[i] = x[i].astype(float)
x.fillna(x.median(),inplace=True) 
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.025)
#begin the model


model=keras.models.Sequential()
model.add(keras.layers.Dense(150,input_dim = 17, activation=tf.nn.relu))
#model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(100,input_dim = 17, activation=tf.nn.relu))
model.add(keras.layers.Dense(50,input_dim = 17, activation=tf.nn.relu))
model.add(keras.layers.Dense(10,input_dim = 17, activation=tf.nn.relu))
model.add(keras.layers.Dense(5,input_dim = 17, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # specifiying hyperparameters
xtrain_tensor = tf.convert_to_tensor(xtrain, dtype=tf.float32)
ytrain_tensor = tf.convert_to_tensor(ytrain, dtype=tf.float32)
model.fit(xtrain_tensor , ytrain_tensor , epochs=100, batch_size=128, validation_split = 0.15, shuffle=True, verbose=2) # load the model
#es = tf.keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', verbose=1,patience=5)
model.save('NephrologistLite') # save the model with a unique name
myModel=tf.keras.models.load_model('NephrologistLite')  # make an object of the model