Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

Md Mehedi Hasan; Senjuti Rahman; Ajay Krishno Sarkar

doi:10.36948/ijfmr.2023.v05i01.1564

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

Author(s)	Md Mehedi Hasan, Senjuti Rahman, Ajay Krishno Sarkar
Country	Bangladesh
Abstract	Deep Learning (DL) and Machine Learning (ML) have the great prospect to play a significant role in the medical field in disease prediction. The tumor or cancer is one of the major health issues that each nation is currently dealing with, and it is the topic of this essay. The prediction of unidentified primary tumors in the dataset is delineated in this paper. Given that it provides significantly higher accuracy than binary classifiers, different multiclass classifier such as K-Nearest Neighbor (KNN), CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, Ada Boost Classifier, Decision Tree Classifier, SVM - Linear Kernel, Naive Bayes and Deep neural networks (DNN1, DNN2, and DNN3) are used to categorize multiclass datasets available in the UCI machine learning repository. Among the stated machine learning classifiers, the k-Nearest Neighbor (KNN) had the highest classification accuracy of 92.92%. The three layer deep neural network (DNN2), among deep learning techniques, had produced the best accuracy of 97.66% using the chosen features as input. The gathered results from this work showed that deep neural networks outperformed machine learning techniques.
Keywords	Tumors, Classifiers, KNN, DNN, Performance Parameters
Field	Biology > Medical / Physiology
Published In	Volume 5, Issue 1, January-February 2023
Published On	2023-02-16
Cite This	Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors - Md Mehedi Hasan, Senjuti Rahman, Ajay Krishno Sarkar - IJFMR Volume 5, Issue 1, January-February 2023. DOI 10.36948/ijfmr.2023.v05i01.1564
DOI	https://doi.org/10.36948/ijfmr.2023.v05i01.1564
Short DOI	https://doi.org/grtwp2

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5 Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

Share this