Comparison and Analysis of Liver Cancer Prediction Using ML

Authors

  • Dr. J Siva Prashanth  Assistant Professor, Computer Science and Engineering, Anurag group of Institutions, Hyderabad, Telangana, India
  • Vodapally Sriharsha  B. Tech, Computer Science and Engineering, Anurag group of Institutions, Hyderabad, Telangana, India
  • Salkapuram Sai Chaithanya Teja  B. Tech, Computer Science and Engineering, Anurag group of Institutions, Hyderabad, Telangana, India
  • Mohd Abdul Jabbar  B. Tech, Computer Science and Engineering, Anurag group of Institutions, Hyderabad, Telangana, India

DOI:

https://doi.org/10.32628/IJSRST52310216

Keywords:

Machine Learning, Logistic Regression, Naive Bayes, Random Forest.

Abstract

In diagnosis centers the importance of detecting a cancer on time is vital. With the help of tools like x-ray, MRI machine, medical professionals can detect somatic mutations easily (a somatic mutation is an acquired change in a genetic code of one or more cells). Here we chose a disease that is liver cancer. This deployed model is given data via google Collab, then analyzed in real-time with machine learning model which was pretrained and the result is shown in the google Collab. Models that are used in our project are Logistic regression, Naive Bayes classifier and Random Forest etc., is used to carry out computation for prediction. And we compare these machine learning models accuracies. But we got good accuracy for machine learning model (random forest classifier). Early detection can help in identifying the risk of liver cancer. Our model is helpful for doctors to give timely medications for treatment.

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International Journal of Scientific Research in Science and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
Dr. J Siva Prashanth, Vodapally Sriharsha, Salkapuram Sai Chaithanya Teja, Mohd Abdul Jabbar "Comparison and Analysis of Liver Cancer Prediction Using ML " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.192-198, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST52310216