Implementation of Different Data Mining Algorithms with Neural Network

The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

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Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i6.pp1569-1576 ◽

2015 ◽

Vol 5 (6) ◽

pp. 1569 ◽

Cited By ~ 13

Author(s):

Moloud Abdar ◽

Sharareh R. Niakan Kalhori ◽

Tole Sutikno ◽

Imam Much Ibnu Subroto ◽

Goli Arji

Keyword(s):

Neural Network ◽

Data Mining ◽

Decision Tree ◽

Heart Diseases ◽

Support Vector ◽

Data Mining Algorithm ◽

Network Support ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Analysis Models

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.

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Application of Neural Network Overview In Data Mining

International Journal of Engineering and Emerging Technology ◽

10.24843/ijeet.2017.v02.i01.p19 ◽

2017 ◽

Vol 2 (1) ◽

pp. 94

Author(s):

Rifky Lana Rahardian ◽

Made Sudarma

Keyword(s):

Neural Network ◽

Data Mining ◽

Neural Networks ◽

High Quality ◽

Data Mining Algorithms ◽

Large Databases ◽

Mining Algorithms

Data Mining is the term used to describe the process extract value / information from the database. Four things are needed in order to effectively data mining: data that has a high quality, right of data, examples of which are adequate, and the correct device. To obtain valuable information in the required data mining algorithms applied in data mining in large databases. There are a lot of complex algorithms in data mining. One is the so-called Neural networks have an important role in data mining.

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A Review Study on Data Mining Algorithms for Prediction Diseases

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0340 ◽

2020 ◽

pp. 504-507

Keyword(s):

Neural Network ◽

Data Mining ◽

Support Vector Machine ◽

Support Vector ◽

Healthcare Industry ◽

Network Support ◽

Data Mining Algorithms ◽

Review Study ◽

Using Data ◽

Mining Algorithms

The healthcare industry assembles massive volume of healthcare information or data that circulate the information into useful data. In everyday life several factors that affect the human diseases. Hospitals are producing large amount of information related to patients. This paper describes the various data mining algorithms such as neural network, support vector machine, KNN, decision tree etc. and provides an overall brief of the existing work. The major advantage of using data mining is that to identify the structures.

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Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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