Bone Tumor Detection Using Machine Learning

Dev Omprakash Paliya

Title:
Bone Tumor Detection Using Machine Learning

Authors:
Dev Omprakash Paliya

Cite This Article :

Dev Omprakash Paliya "Bone Tumor Detection Using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.126-136, URL: https://www.ijtsrd.com/papers/ijtsrd101611.pdf

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Abstract :
Bone tumor detection is an important area of research in the medical and healthcare fields because we need to find and diagnose tumors early and accurately. Medical datasets have a lot of clinical attributes that can be analyzed using Machine Learning techniques to help doctors make diagnoses and decisions. With more Artificial Intelligence being used in healthcare we are using models to look at clinical data find patterns in tumors and make diagnoses more accurate while reducing mistakes made by humans.We are still talking about bone tumor detection. Systems that use data to make healthcare decisions have led to the creation of classification models that can tell the difference between tumor and nontumor cases based on clinical features. However the quality of how we prepare and medical datasets is very important for how well the models work.In this study we used a machine learning framework to classify bone tumors using a dataset with 570 samples. We cleaned the data scaled the features and split the data into training and testing sets to make sure the evaluation was fair. We used algorithms like Logistic Regression, Random Forest and Support Vector Machine to compare and find the best model for bone tumor detection. We did steps including cleaning the data looking at the data analyzing how features are related training the model and evaluating how well it worked. We looked at how accurate the model was made a confusion matrix and used ROC-AUC metrics to evaluate the model. The results showed that the model was very accurate with an accuracy of 0.99 and an AUC score of 0.99 which means it is very good at telling the difference, between malignant bone tumor cases. These results suggest that if we have prepared structured clinical data and use supervised machine learning we can classify bone tumors reliably and efficiently.

Keywords :
Bone Tumor Detection; Machine Learning; Clinical Tabular Data; Supervised Learning; Classification; ROC-AUC.

Publication Details:

Unique Identification Number : IJTSRD101611

Published In : Special Issue | Smart Innovations in Computer Science and Applications, March 2026

Page Number(s) : 126-136

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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