Early prediction of systemic lupus erythematosus using hybrid K-Means J48 decision tree algorithm

Abstract

The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the preprocessed data. The work-flow has been proposed to show the execution of the algorithm.

Diabetes Detection Using Support Vector Machine

“…Bayu Adhi Tama, et.al (2018) [3] This essay examines diabetes, a chronic condition that has a serious global impact. Worldwide, 285 million individuals have diabetes, in relation to the World Diabetes Federation (IDF) .…”

A Deep learning of Autism Spectrum Disorder using Naïve Bayes, IBk and J48 classifiers

“…The WEKA tool provides a number of options like tree pruning. Most of the classification algorithms perform recursively until every single leaf is pure to make sure the classification [9] of the data to be as perfect as possible.…”

“…According to the Centre for disease control and prevention, there is a sustainable growth in the number of children diagnosed with Autism disorder and 1 among 68 Children under the age of 8 in the United States of America is diagnosed with autism and According to WHO [5], about 1 out of every 160 children has ASD. Data mining plays an important role to classify and predict the disease in the early stage [9].…”