Mesothelioma Disease Detector

I recently found another disease detection dataset on the UC Irvine Machine Learning Repository and decided to have a go at this one. However this dataset is not used that often for disease detection as I have never heard of it before and never even came across it before discovering it while reading a research paper. I’d like to shed some light on this dataset and present the Mesothelioma Disease Detector Web App.

import streamlit as st
import pandas as pd
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import seaborn as sns
from PIL import Image
from sklearn import preprocessing

After this I have included a slight description of the app as a string which includes the dataset resource and the developers. I’ve also put in the dataset and included headings that I’ve used.

st.markdown('''
#  Mesothelioma Detector 
This app detects if you have Mesothelioma based on Machine Learning!
- App built by Pranav Sawant and Anshuman Shukla of Team Skillocity.
- Dataset Creators: Abdullah Cetin Tanrikulu from Dicle University, Faculty of Medicine, Department of Chest Diseases, 21100 Diyarbakir, Turkey
- Orhan Er from Bozok University, Faculty of Engineering, Department of Electrical and Electronics Eng., 66200 Yozgat, Turkey
- Note: User inputs are taken from the sidebar. It is located at the top left of the page (arrow symbol). The values of the parameters can be changed from the sidebar.  
''')
st.write('---')

df = pd.read_csv(r'Mesothelioma data set.csv')
st.sidebar.header('Patient Data')
st.subheader('Training Dataset')
st.write(dfnew.describe())

After this we need to train and test our data. For the purpose of this app, I’ve used the test size and train size as 60% and 40% respectively.

x = dfnew.drop(['Outcome'], axis = 1)
y = dfnew.iloc[:, -1]
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.6, random_state = 0)
lab_enc = preprocessing.LabelEncoder()
training_scores_encoded = lab_enc.fit_transform(y_train)

Once we’re done with this, we need to define the user report and the user report data depending on the various parameters given in the training dataset. For this particular dataset, the parameters are Age, Platelet count, Blood Lactic Dehydrogenise, Alkaline Phosphatise, Total Protein, Albumin, Glucose, Pleural Lactic Dehydrogenise, Pleural Protein, Pleural Albumin, Pleural Glucose and C-Reactive Protein.

def user_report():
  Age = st.sidebar.slider('Age', 0,100, 54)
  Platelet_Count = st.sidebar.slider('Platelet Count', 0,3500, 315 )
  Blood_Lactic_Dehydrogenise = st.sidebar.slider('Blood Lactic Dehydrogenise', 0,1000, 20 )
  Alkaline_Phosphatise = st.sidebar.slider('Alkaline Phosphatise', 0,500, 92 )
  Total_Protein = st.sidebar.slider('Total Protein', 0.0,10.0, 5.1 )
  Albumin = st.sidebar.slider('Albumin', 0.0,8.0, 1.1 )
  Glucose = st.sidebar.slider('Glucose', 0,500, 10 )
  Pleural_Lactic_Dehydrogenise = st.sidebar.slider('Pleural Lactic Dehydrogenise', 0,8000, 5 )
  Pleural_Protein = st.sidebar.slider('Pleural Protein', 0.0,8.0, 6.5 )
  Pleural_Albumin = st.sidebar.slider('Pleural Albumin', 0.0,6.0, 4.2 )
  Pleural_Glucose = st.sidebar.slider('Pleural Glucose', 0,120, 44)
  Creactive_Protein = st.sidebar.slider('C-reactive Protein', 0,120, 6)
  
  
  
  
  
  
  user_report_data = {
      'Age':Age,
      'Platelet_Count':Platelet_Count,
      'Blood_Lactic_Dehydrogenise':Blood_Lactic_Dehydrogenise,
      'Alkaline_Phosphatise':Alkaline_Phosphatise,
      'Total_Protein':Total_Protein,
      'Albumin':Albumin,
      'Glucose':Glucose,
      'Pleural_Lactic_Dehydrogenise':Pleural_Lactic_Dehydrogenise,
      'Pleural_Protein':Pleural_Protein,
      'Pleural_Albumin':Pleural_Albumin,
      'Pleural_Glucose':Pleural_Glucose,
      'Creactive_Protein':Creactive_Protein,
        
  }
  report_data = pd.DataFrame(user_report_data, index=[0])
  return report_data





user_data = user_report()
st.subheader('Patient Data')
st.write(user_data)

After we’ve defined the user report, we need to run the algorithm.

rf  = RandomForestClassifier()
rf.fit(x_train, training_scores_encoded)
user_result = rf.predict(user_data)

Now we finally come to my most favourite part of these web apps: Visualizations. I have been experimenting a lot with a number of visualization libraries but some of them really stand out for me and I use them often in my apps. So here as a convention I’ve used blue colour for healthy patients and the colour red for unhealthy patients.

st.title('Graphical Patient Report')



if user_result[0]==0:
  color = 'blue'
else:
  color = 'red'

We start off with Platelet Count and code in its visualizations. Here I’ve basically plotted a seaborn scatterplot with age on the x axis and the values of the Platelet Count parameter on the y axis. I have used the purple palette and have scaled the axes according to the data. A value of 0 represents a healthy case whereas a value of 1 represents an unhealthy case.

st.header('Platelet Count Value Graph (Yours vs Others)')
fig_Radius = plt.figure()
ax3 = sns.scatterplot(x = 'Age', y = 'Platelet_Count', data = df, hue = 'Outcome' , palette='Purples')
ax4 = sns.scatterplot(x = user_data['Age'], y = user_data['Platelet_Count'], s = 150, color = color)
plt.xticks(np.arange(0,100,5))
plt.yticks(np.arange(0,3500,175))
plt.title('0 - Healthy & 1 - Unhealthy')
st.pyplot(fig_Radius)

Now that we are done with one parameter, we can very easily do this same for the other parameters as well. Just replace the above code snippet with that of the other parameters and you are set to go. I will leave this as an exercise for you’ll and if you have any queries regarding it, please do ask. After completing the visualizations for all the parameters, we are finally ready to display the outcome and the prediction. I have given the outcome in the form of a user report.

st.subheader('Your Report: ')
output=''
if user_result[0]==0:
  output = 'Congratulations, you do not have  Mesothelioma'
else:
  output = 'Unfortunately, you do have Mesothelioma'
st.title(output)

Next, I have duly given the dataset credits to the respective owners and authorities in charge of this dataset . I have also mentioned where I received the dataset from (UCI Machine Learning Repository) and have cited the original creators of this dataset for their commendable work.

To cap up this web app, I’ve given a disclaimer that I give for all my BioTechnology and medical applications of data science that this is an application based on one particular dataset so we cannot use it universally. I have also attached the logo of Skillocity at the end.

So that’s it from this web app and I’ll see you soon with another fun application of Machine Learning / Data Science and give some interesting insights. Hasta pronto !