Integration of Machine Learning Algorithms in Genomic Data for Accurate Cancer Diagnosis and Prognosis

Authors

  • Girija Gireesh Chiddarwar Department of Computer Engineering, Marathwada Mitra Mandal's College of Engineering, Pune
  • Chhaya Santosh Gosavi Department of Computer Engineering, MKSSS's Cummins College of Engineering for Women, Karvenagar, Pune
  • Smita M. Chaudhari Department of Computer Engineering, Marathwada Mitra Mandal's College of Engineering Pune

Keywords:

Breast Carcinoma, Computer-Aided Diagnosis (CAD), Machine Learning Algorithms, Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN)

Abstract

The essential driver of disease-related fatalities around the world, bosom carcinoma, is a huge issue that this study endeavors to address. Each year, nearly 900,000 people die; improved results rely upon early identification and exact finding. The challenges in separating among harmless and threatening cancers feature the need for refined methods. Utilizing AI calculations, this study recommends a PC Helped Conclusion (computer aided design) strategy that partitions patients into three gatherings: non-dangerous, no harm, and threatening. As effective classifiers, the review centers around Irregular Backwoods (RF), Backing Vector Machines (SVM), and Convolutional Brain Organizations (CNN). Intriguingly, the study even goes so far as to preprocess images from mammograms in order to increase classification accuracy. By going past customary paired classification, the exploration propels the field of disease identification by giving a more nuanced strategy to additional exact prognostic assessments and maybe bringing the passing rate related down to bosom malignant growth.

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References

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Published

24.03.2024

How to Cite

Chiddarwar, G. G. ., Gosavi, C. S. ., & Chaudhari, S. M. . (2024). Integration of Machine Learning Algorithms in Genomic Data for Accurate Cancer Diagnosis and Prognosis. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 01–06. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4943

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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