Mesothelioma Disease Detector

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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.

This web app detects if you have Mesothelioma or not depending upon various parameters such as Platelet Count, Blood Lactic Dehydrogenise, Alkaline Phosphatise, Total Protein, Albumin, Glucose, Pleural Lactic Dehydrogenise, Pleural Protein, Pleural Albumin, Pleural Glucose and C-reactive Protein. I'll explain more about these parameters later on in the article. This web app works on the Random Forest Classifier algorithm and is majorly coded in python.  Mesothelioma is a type of cancer that occurs in the thin layer of tissue that covers the majority of our internal organs (mesothelium). Mesothelioma is an aggressive and deadly form of cancer.

We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It's one of the best hosting services I've used and it's great for quick and easy deployment of web apps. The app is coded in python. 

The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. We trained the dataset using 20% of it as our test size. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having mesothelioma and patients not having mesothelioma). 

This web app was a learning curve for us and has improved our knowledge about Machine learning significantly. We hope to deploy more apps in the future and share them with you. Feel free to add onto this project and don't hesitate to drop by any suggestions. The link for the Mesothelioma Disease Detector web app is as follows : 

https://share.streamlit.io/skillocity/mesothelioma-/main/app.py

About the dataset: Malignant mesotheliomas (MM) are very aggressive tumors of the pleura. These tumors are connected to asbestos exposure,

However it may also be related to previous simian virus 40 (SV40) infection and quite possible for genetic predisposition.
Molecular mechanisms can also be implicated in the development of mesothelioma.
Rural living is associated with the development of mesothelioma. Soil mixtures containing asbestos, known as
‘white-soil’ or ‘corak’ can be found in Anatolia, Turkey and ‘Luto’ in Greece.
Mesothelioma’s disease data set were prepared at Dicle University Faculty of Medicine in Turkey.
Three hundred and twenty-four Mesothelioma patient data. In the dataset, all samples have 34 features.

Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have Mesothelioma or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

Mesothelioma Awareness Day is Sept. 26. On this day, patients, family members, doctors and the mesothelioma community raise awareness of the rare cancer to help find a cure. Supporters wear blue and may wear mesothelioma awareness wristbands or ribbons.t was established in 2004 by the Mesothelioma Applied Research Foundation. It wasn’t until 2010 that Congress first declared September 26 as National Mesothelioma Awareness Day. On that note, lets raise awareness for Mesothelioma and show our support for Mesothelioma awareness and help many patients around the world. 


Comments

  1. RaghavJuly 2, 2021 at 11:00 PM

    Awesome, from where did you get to know this dataset. I couldn't find it on Kaggle lol

    ReplyDelete
    Replies
    1. Pranav SawantJuly 4, 2021 at 10:23 PM

      Well originally came across it while reading a research paper and discovered that its open source on the UCI ML repo. Yeah apparently this dataset ain't available on Kaggle

      Delete

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