Liver Cancer Identification Grid Search RFC Model using Machine Learning

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Sunita Kumari
Gourav Mitawa

Abstract

Liver is essential to the body's digestion of sugar and fats, absorption, and immunological system. This substance is present in almost everything a man takes in, breathes, or absorbs through his skin. Liver disorders are a significant health burden. It is increasing daily and is difficult to detect in its early stages since the liver may function normally even when partially damaged. Doctors have widely employed machine learning algorithms to diagnose liver illness in order to increase the efficiency of medical diagnosis. The study's primary aim is to evaluate how machine learning algorithms may be used to prevent postponing medical care, accurately diagnose liver illness, and minimize the number of erroneous diagnoses provided to sick patients. The main objective is to ensure that liver patients receive an accurate diagnosis as soon as possible.

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How to Cite
Sunita Kumari, & Gourav Mitawa. (2023). Liver Cancer Identification Grid Search RFC Model using Machine Learning . International Journal of New Practices in Management and Engineering, 12(1), 01–05. Retrieved from https://www.ijnpme.org/index.php/IJNPME/article/view/196
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References

J. Rehm, A. v. Samokhvalov, and K. D. Shield, “Global burden of alcoholic liver diseases,” Journal of Hepatology, vol. 59, no. 1, pp. 160–168, 2013, doi: 10.1016/j.jhep.2013.03.007.

[2.] B. F, F. J, S. I, S. RL, T. LA, and J. A, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: a cancer journal for clinicians, vol. 68, no. 6, pp. 394–424, Nov. 2018, doi: 10.3322/CAAC.21492.

H. Kahramanli and N. Allahverdi, “Rule extraction from trained adaptive neural networks using artificial immune systems,” Expert Systems with Applications, vol. 36, no. 2 PART 1, pp. 1513–1522, 2009, doi: 10.1016/J.ESWA.2007.11.024.

R. H. Lin and C. L. Chuang, “A hybrid diagnosis model for determining the types of the liver disease,” Computers in Biology and Medicine, vol. 40, no. 7, pp. 665–670, 2010, doi: 10.1016/j.compbiomed.2010.06.002.

R. Vahini and B. Sathees Kumar, “A survey on liver disorder using classification techniques in data mining,” International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 2796–2799, 2015.

S. Ambesange, R. Nadagoudar, R. Uppin, V. Patil, S. Patil, and S. Patil, “Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques,” Proceedings of B-HTC 2020 - 1st IEEE Bangalore Humanitarian Technology Conference, Oct. 2020, doi: 10.1109/B-HTC50970.2020.9297949.

A. Kalsoom, A. Moin, M. Maqsood, I. Mehmood, and S. Rho, “An Efficient Liver Tumor Detection using Machine Learning,” pp706–711,2021,doi: 10.1109/csci51800.2020.