Prediction of recurrence cancer using J48 algorithm

Bhargava, Neeraj; Sharma, Sakshi; Purohit, Renuka; Rathore, Pramod Singh

doi:10.1109/cesys.2017.8321306

Cited by 22 publications

(20 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…J48 works well with both continuous and discrete attributes and data with missing attribute values. It also gives an option for pruning trees after creation [34]. Examples of decision trees can be seen in Figure 2.…”

Section: J48 Decision Treementioning

confidence: 99%

Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

2021

View full text Add to dashboard Cite

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

show abstract

Section: J48 Decision Treementioning

confidence: 99%

Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

2021

View full text Add to dashboard Cite

show abstract

“…The algorithms used to create a decision tree in the literature are ID3, C4.5, C5.0 and CART. Besides, the J48 algorithm that is developed by the WEKA project team and used to create decision trees, is an open-source Java implementation of the C4.5 algorithm (Bhargava et al 2018;Yadav and Chandel 2015). The J48 algorithm is applied with the WEKA software to select the input parameters of the ANNs model.…”

Section: Applicationmentioning

confidence: 99%

“…The J48 algorithm helps create easy-to-understand models and uses categorical and continuous values (Aljawarneh, Yassein, and Aljundi 2019). J48 creates decision trees from the training dataset, taking advantage of the fact that every feature of the data can be used to make a decision by dividing it into smaller subsets (Bhargava et al 2018). The algorithm offers a method, known as imputation, identifies missing values according to the existing values and allows the problem of missing values to be solved (Aljawarneh, Yassein, and Aljundi 2019).…”

Section: Applicationmentioning

confidence: 99%

A New Approach for Rule Estimation of Fuzzy Inference System: A Case Study for Public Transport Maintenance System

Erdoğan

Kaya

2020

Konya Journal of Engineering Sciences

View full text Add to dashboard Cite

The increase in the population and the high amount of individual vehicle usage in the big cities brought traffic congestion and environmental problems. Additionally, these issues have also some negative effects on the public transport systems (PTSs). In this respect, the analysis of PTS is critical and important for both city life and people. It is possible that the failures in PTS can lead to many problems. Disruption of daily life, loss of lives and property or damage to the environment are only just a few of these problems. In this context, effective maintenance planning for PTSs is so crucial. In this study, the rule estimation for a fuzzy rule-based system (FRBS) which takes into consideration many factors for the maintenance planning of PTSs is discussed. The rule-based system for maintenance planning of Bus Rapid Transit System (BRT) will be highly effective for the prediction of failures for PTSs and the correct actions to be taken. Rule estimation for this system is aimed to increase the precision and flexibility of maintenance procedures. In this context, a model based on artificial neural networks (ANNs) has been developed and used in rule estimation for FRBS. For this aim, ten cases that are not in the rule base system are estimated and the results of the fuzzy rule-based maintenance inference system for the relevant inputs are revealed. Thus, it has been shown that ANNs can be used effectively for the analysis of rules that are not included in the current rule-based maintenance system.

show abstract

“…For C4.5 algorithm J48 is a java execution provided by Weka tool. Based on the attributes such as node-caps, era, lump size, menopause, illuminate etc to predict the repeated proceedings an author scrutinizes the verdict tree created by the algorithm using 10-fold irritable authentication technique [10].…”

Section: B Kranthi Kiranmentioning

confidence: 99%

Implementation of Tumor Prediction System using Classification Algorithms

kiran

2019

IJEAT

View full text Add to dashboard Cite

As the huge volume of healthcare data was being unused, recent researchers were focused on predicting the many diseases by analyzing the past patient records. In continuation with that, there are lot of researches focused on predicting the tumor on the human body. In this research, two widely used classification algorithms called Naïve Bayes and Random tree were considered for implementation and analysis with the UCI Machine learning Tumor data set. The data cleaning technique called “Replace Missing Values” in the WEKA tool has been considered for cleaning the data. The implementation has been done with the original dataset and the cleaned dataset. Finally, it is found that the Random tree algorithm is performed well with improved accuracy and reduced error rate. The accuracy obtained before data cleaning is 90.8333% and after data cleaning is 93.3333 %. Similarly, the error rates were reduced reasonably and they are 9.1667 % before data cleaning and 93.3333 % after data cleaning. In future, the data cleaning techniques has to be tuned well to improve the accuracy further.

show abstract

Prediction of recurrence cancer using J48 algorithm

Cited by 22 publications

References 1 publication

Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

A New Approach for Rule Estimation of Fuzzy Inference System: A Case Study for Public Transport Maintenance System

Implementation of Tumor Prediction System using Classification Algorithms

Contact Info

Product

Resources

About