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All Journal Asean Journal of Science and Engineering (AJSE)
Vijayarani, S.
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Engineering Materials Science & Nanotechnology

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A Hybrid Classification Algorithm for Abdomen Disease Prediction Vijayarani, S.; Sivamathi, C.; Tamilarasi, P.
ASEAN Journal of Science and Engineering Vol 3, No 3 (2023): AJSE: December 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ajse.v3i3.45677

Abstract

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.
Frequent Items Mining on Data Streams using Matrix and Scan Reduced Indexing Algorithms Vijayarani, S.; Sivamathi, C.; Prassanalakshmi, R.
ASEAN Journal of Science and Engineering Vol 3, No 2 (2023): AJSE: September 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ajse.v3i2.45345

Abstract

A data stream is used for handling dynamic databases, in which data can arrive continuously without limit. Association rule mining is a data mining technique, used to find the association between the data items in the databases. To generate association rules, frequent items are to be identified from the transactional database. Normally, in data mining, frequent-item-generation algorithms scan the database multiple times. But this is impossible in data streams because it handles dynamic databases. Hence, there is a need to develop a new algorithm, which reduces the number of database scans. In this work, two new algorithms named Scan-Reduced Indexing and Matrix algorithm are proposed for generating frequent itemsets in data streams. Performances of both algorithms are compared based on the execution time and the number of frequent items generated. Experimental results show that the performance of the Scan-Reduced Indexing algorithm is more efficient than that of the Matrix algorithm.
Co-Authors Prassanalakshmi, R. Sivamathi, C. Tamilarasi, P.