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Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer

K Chandrashekar1, Anagha S Setlur1, Adithya Sabhapathi C1

  • 1Department of Biotechnology, R V College of Engineering, Bengaluru, Karnataka, India.

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|January 30, 2023
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Summary
This summary is machine-generated.

This study developed a machine learning model for early cancer diagnosis. The random forest model achieved 82% accuracy in classifying five cancer types from whole exome data.

Keywords:
Cancer diagnosisMCCSMOTEWeb applicationclassification modelsupervised machine learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Oncology

Background:

  • Accurate cancer diagnosis is crucial for effective treatment and patient outcomes.
  • Decision support systems (DSS) can aid clinicians in making informed diagnostic decisions.
  • Whole exome sequencing generates vast amounts of data for cancer analysis.

Purpose of the Study:

  • To design and evaluate a machine learning classification model for predicting five distinct cancer types.
  • To improve cancer classification accuracy using feature selection and dataset balancing techniques.
  • To develop a user-friendly web application for cancer classification.

Main Methods:

  • Supervised machine learning algorithms (KNN, SVM, Decision Tree, Naïve Bayes, Random Forest) were initially employed.
  • Feature selection identified 16 essential features for model enhancement.
  • SMOTE (Synthetic Minority Over-sampling Technique) was used to balance imbalanced datasets.
  • Two approaches were tested, with the first involving training on all features and the second on a subset.
  • The Random Forest model was validated using Matthew's Correlation Coefficient (MCC).

Main Results:

  • The Random Forest model, after feature selection and dataset balancing, achieved 82% accuracy, correctly classifying all five cancer types.
  • The Area Under the Curve (AUC) for the Random Forest model was closer to 1 compared to the Decision Tree model.
  • MCC scores of 0.7796 and 0.9356 (cross-validation) were obtained for the optimized Random Forest model.
  • The second approach, while yielding a high MCC of 0.9365, only correctly predicted 2 cancer types.

Conclusions:

  • The developed Random Forest-based classification model demonstrates high accuracy and potential for early cancer diagnosis.
  • Feature selection and data balancing are critical for improving the performance of cancer classification models.
  • The deployed Streamlit web application provides an accessible tool for cancer classification, aiding researchers and clinicians.