Improved J48 Classification Algorithm for the Prediction of Diabetes

Kaur, Gaganjot; Chhabra, Amit

doi:10.5120/17314-7433

Cited by 221 publications

(115 citation statements)

References 8 publications

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“…The main goal of pruning is to remove the classification errors and do the generalization. This algorithm is more flexible and accurate when compared to other decision tree algorithms [13].…”

Section: J48 Algorithmmentioning

confidence: 99%

A Study on Machine Learning Algorithm in Medical Diagnosis

Pavithra¹,

Jayanthi²

2018

ijarcs

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: Machine learning is a method of optimizing the performance criterion using the past experience. It builds the mathematical model by using the theory of statistics, as the main task is to infer from the samples provided. The algorithm uses computational methods to get the information directly from the data. They are mainly used in medical diagnosis for making critical decisions, as the data in the medical field is huge and the accuracy of the diagnosis depends on considering the huge data of the patients. ML improves the accuracy of the diagnostic of the disease. It also provides automatic learning techniques for predicting the common patterns from the realistic data. There are different ML algorithms, the appropriate method has to be chosen based on their performance. This paper focuses on the use of different machine learning algorithms like Support Vector Machine, Naïve Bayesian, J48, Random Forest etc. for accurate medical diagnosis.

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Section: J48 Algorithmmentioning

confidence: 99%

A Study on Machine Learning Algorithm in Medical Diagnosis

Pavithra¹,

Jayanthi²

2018

ijarcs

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“…Finally, the research highlights the causes of lupus and the method to predict the disease in advance and to extend the life span of the patients. By processing the data with K-means [4,5] and J48 algorithm [6,7] has castoff to predict lupus in advance.…”

Section: Applying Data Mining To Predict Lupusmentioning

confidence: 99%

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

Gomathi¹,

Narayani²

2017

IJET

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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.

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“…This algorithm it generates the rules from which particular identity of that data is generated. The objective is progressively generalization of a decision tree until it gains equilibrium of flexibility [22].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Classification Techniques on Energy Efficiency Dataset

Toprak

Koklu

Toprak

et al. 2017

ijisae

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Abstract:The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it's been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.

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Improved J48 Classification Algorithm for the Prediction of Diabetes

Cited by 221 publications

References 8 publications

A Study on Machine Learning Algorithm in Medical Diagnosis

A Study on Machine Learning Algorithm in Medical Diagnosis

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

Comparison of Classification Techniques on Energy Efficiency Dataset

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