Early Phase, Multi Diseases Detection, Using AI & Intelligent Hybrid Supervised Machine Learning Classifier Model

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : Anandkumar A. Sutariya, Dushyantsinh B. Rathod

10.14445/23488549/IJECE-V11I11P105

How to Cite?

Anandkumar A. Sutariya, Dushyantsinh B. Rathod, "Early Phase, Multi Diseases Detection, Using AI & Intelligent Hybrid Supervised Machine Learning Classifier Model," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 45-53, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P105

Abstract:

The healthcare industry generates vast amounts of data processed using specific techniques. Data mining is key to the healthcare industry's illness prediction process. The patient should be required to undergo a number of tests in order to diagnose the condition. The two main organ diseases that affect humans are kidney and liver diseases. Regardless, the two well-known conditions affecting people nowadays are Chronic Kidney Disease (CKD) and Chronic Liver Disease (CLD). A small number of people pass away these days due to chronic liver and kidney diseases. These are the most common illnesses on the earth and are real infections. In addition, the progressive degeneration of the kidney and liver is referred to as chronic kidney disease and chronic liver disease, respectively. A promising technique that aids in early disease identification and may support medical practitioners in their decision-making is machine learning. This study examines data mining techniques that can be applied to forecast conditions such as liver and kidney disorders. Using a dataset for training and testing, we compute the precision of machine learning calculations for predicting kidney and liver infection. We found that the proposed hybrid classifier model, K-Nearest Neighbor, Naïve Bayesian, and Decision Tree methods all had superior accuracy. This research aims to develop a framework to more accurately forecast a patient's risk of developing kidney and liver problems. The suggested methodology yielded higher results than other methods under consideration, scoring 99.24% and 99.96 for CKD and CLD, respectively. Furthermore, data pick-up and other attribute evaluators have been included to show the framework's tall execution with the fewest possible features.

Keywords:

Chronic Kidney Disease, Chronic Liver Disease, Classification, Prediction, Hybrid classifier model, K-Nearest Neighbor, Decision Tree, Bayesian classifier, Machine Learning and AI techniques.

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