scholarly journals Predictive Factors of Infant Mortality Using Data Mining in Iran

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mahmoud Hajipour ◽  
Niloufar Taherpour ◽  
Haleh Fateh ◽  
Ebrahim Yousefi ◽  
Koorosh Etemad ◽  
...  
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Infant Mortality ◽  
Rural Areas ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naïve Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7% of infant mortalities occurred in rural areas, that 55.6% of them were boys. Moreover, Naïve Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality.

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

scholarly journals Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

2020 ◽  
Vol 1641 ◽  
pp. 012068
Author(s):  
Diah Puspitasari ◽  
Kresna Ramanda ◽  
Adi Supriyatna ◽  
Mochamad Wahyudi ◽  
Erma Delima Sikumbang ◽  
...  
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preterm Birth ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithms ◽  
Artificial Neural ◽  
Mining Algorithms

scholarly journals Migrating From Data Mining to Big Data Mining

2018 ◽  
Vol 7 (3.4) ◽  
pp. 13
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Response Time ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Big Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with  pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples.  Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.  

Performance Analysis of Data Mining Algorithms

2019 ◽  
Vol 16 (9) ◽  
pp. 3849-3853
Author(s):  
Dar Masroof Amin ◽  
Atul Garg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Future Trend ◽  
Easy Access ◽  
Support Vector ◽  
Linear Discriminant ◽  
Data Mining Algorithms ◽  
Data Files ◽  
Using Data ◽  
Mining Algorithms

The globalisation of Internet is creating enormous amount of data on servers. The data created during last two years is itself equivalent to the data created during all these years. This exponential creation of data is due to the easy access to devices based on Internet of things. This information has become a source of predictive analysis for future happenings. The versatile use of computing devices is creating data of diverse nature and the analysts are predicting the future trend using data of their respective domain. The technology used to analyse the data has become a bottleneck over the time. The main reason behind this is that the rate with which the data is getting created is much more than the technology used to access the same. There are various mining techniques used to explore the useful information. In this research there is detailed analysis of how data is used and perceived by various data mining algorithms. Mining algorithms like Naïve Bayes, Support Vector Machines, Linear Discriminant Analysis Algorithm, Artificial Neural Networks, C4.5, C5.0, K-Nearest Neighbour are analysed. The input data used in these algorithms is big data files. This research mainly focuses on how the existing data algorithms are interacting with big data files. The research has been done on twitter comments.

scholarly journals AUMENTANDODESEMPENHO DEALGORITMOSDEMINERAÇÃODEDADOSUTILIZANDOAPLATAFORMACUDA

2018 ◽  
pp. 90-102
Author(s):  
Matheus Varela Ferreira ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Processing ◽  
Processing Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Data Mining Algorithms ◽  
Mining Algorithms

With the increasing need to make decisions in the short term, industry (pharmaceutical, petrochemical, aeronautics and etc.) has been seeking new ways to reduce the time of the data mining process to obtain knowledge. In recent years, many technological resources are being used to mitigate this need, an example is CUDA. CUDA is a platform that enables the use of GeForce GPUs in conjunction with CPUs for data processing, significantly reducing processing time. This work proposes to perform a comparative analysis of the processing time between two versions of some data mining algorithms (Apriori, AprioriAll, Naïve Bayes and K-Means), one running on CPU only and one on CPU in conjunction with GPU through platform CUDA. Through the experiments performed, it was observed that using the CUDA platform it is possible to obtain satisfactory results.

scholarly journals A Review Study on Data Mining Algorithms for Prediction Diseases

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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