A Hybrid Classification Algorithm for Abdomen Disease Prediction

S. Vijayarani, C. Sivamathi, P. Tamilarasi


Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Data mining techniques consist of detection of Anomaly, learning the Association rules, Classification, Clustering, Regression, Time series analysis, and Summarization. In data mining, classification techniques are much popular in medical diagnosis and predicting diseases. Classification techniques are used to predict various diseases such as heart disease, lung cancer, breast cancer, liver diseases, and kidney diseases. The main objective of this work is to predict abdomen diseases like kidney and liver diseases. The work aims to predict liver diseases such as Cirrhosis, Bile Duct, Chronic Hepatitis, Liver Cancer, and Acute Hepatitis using Classification algorithms. The work also aims to predict kidney diseases such as Acute Nephritic Syndrome, Chronic Kidney disease, Acute Renal Failure, and Chronic Glomerulonephritis using Classification algorithms.  This work proposes a novel hybrid classification algorithm called WRFSVM (Weighted Random Forest Support Vector Machine) for the prediction of liver diseases and kidney diseases.


Acute nephritic syndrome; Acute renal failure; Chronic glomerulonephritis, Chronic kidney disease, Classification algorithms, Liver diseases; Liver function test; Random forest; Ripper; SVM

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DOI: https://doi.org/10.17509/ajse.v3i3.45677


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