An Introduction to Non Small Cell Lung Cancer Prediction Model

Contents

1. Introdution
2. Preparing CSV
3. Using End point
   • 3.1 Creating model
   • 3.2 Creating Endpoint Config
   • 3.3 Creating Endpoint
   • 3.4 Invoking Endpoint
   • 3.5. Deleting the Endpoint - Optional
4. Model Output

1. Introdution

Non Small Cell Lung Cancer is the most common type of Lung Cancer and nearly 5% of US population undergo it. It is the second leading cause of cancer death for men and women.Prediction model helps to identify the person suffering from Non Small Cell Lung Cancer by taking into account various observational parameters and demographic details.

2. Preparing CSV

The csv file used in the model has 10 columns viz

• AGE = Person's Age
• 6158-0 = Latex IgE Ab [Units/volume] in Serum
• 71956-7 = Sleep disturbance score
• 718-7 = Hemoglobin [Mass/volume] in Blood
• 6844-5 = Honey bee IgE Ab [Units/volume] in Serum
• 6833-8 = Cat dander IgE Ab [Units/volume] in Serum
• 6768-6 = Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma
• 6690-2 = Leukocytes [#/volume] in Blood by Automated count
• 66534-9 = Percentage area affected by eczema Lower extremity - bilateral [PhenX]
• 66529-9 = Percentage area affected by eczema Trunk [PhenX]

Same kind of CSV file should be fed to the model for best output.

For reference, A sample CSV file is attached in the link. The user only needs to clik on the link to get it downloaded to the local. The data should be in a similar format like the sample csv.

Download sample Data from the link : Data Source for LungCancer

import pandas as pd
df = pd.read_csv(' S3/Sagemaker path of LungCancer.csv file')
df.to_csv("sample.csv",index=False)
df.head(3)

3. Using Endpoint

The model can be directly accessed via the console provided by AWS. However, for a more customized process one can access the model using the below code as well.

3.1 Creating Model

To create a model, import boto3, sagemaker and get the arn of the model package

import boto3
import sagemaker
role = sagemaker.get_execution_role()
smmp = boto3.client('sagemakermp')
modelName='Name of the model'
modelArn = 'Model ARN name'
createHeatIndexResponse = smmp.create_model(ModelName=modelName,\
                             Containers=[{'ModelPackageName': modelArn}],\
                             ExecutionRoleArn=role,\
                             EnableNetworkIsolation=True )

3.2 Creating Endpoint Config

configName ='<Input Configuration Name>'
instanceType = '<Input Instance Type>'
createHeatIndexEndpointConfig = smmp.create_endpoint_config(EndpointConfigName = configName, ProductionVariants = [{'InstanceType':instanceType, 'InitialInstanceCount':1, 'ModelName':modelName, 'VariantName':'xyz'}])

3.3 Creating Endpoint

endpointName = '<Input Endpoint Name>'
createHeatIndexEndpoint = smmp.create_endpoint(EndpointName = endpointName, EndpointConfigName = configName)

3.4 Invoking Endpoint

runtime = boto3.Session().client('runtime.sagemaker')

#Reading Input Data 
with open('sample.csv','rb') as f:
    payload = f.read()

response = runtime.invoke_endpoint(EndpointName = endpointName, ContentType = 'text/csv', Body = payload)
result = response['Body'].read().decode()


#Writing Output Data 
with open('sampleOutput.txt','w') as f:
    f.write(result)

3.5. Deleting the Endpoint - Optional

If you're ready to be done with this notebook, please run the delete_endpoint line in the cell below. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on

sagemaker.Session().delete_endpoint(endpointName)

4. Model Output

with open("./sampleOutput.txt","r") as f:
    sampleResponse =f.read()
    sampleResponse = sampleResponse.split('\n')
sampleResponse