Identifying risk factors for adverse diseases using dynamic rare association rule mining

Borah, Anindita; Nath, Bhabesh

doi:10.1016/j.eswa.2018.07.010

Cited by 69 publications

(34 citation statements)

References 36 publications

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“…Previous research showed that association rule used achieved remarkable performance gain in terms of execution time [16]. However, association rule used involved some computational overhead due to splitting and merging of nodes.…”

Section: Discussionmentioning

confidence: 99%

“…ARIMA was only able to generate the rare item sets and rare association rules. Different from these two techniques, the proposed approach could generate the complete sets (the frequent and the rare item sets) as well as frequent association rules and rare association rules [16].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Previous research introduced an efficient technique to identify the symptoms and risk factors for three adverse diseases: cardiovascular disease, hepatitis and breast cancer, in terms of rare association rules [16]. The results obtained shown that association rule used could achieve remarkable performance gains in terms of execution time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Werdiningsih

Hendradi

Purbandini

et al. 2019

Cybernetics and Information Technologies

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This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Werdiningsih

Hendradi

Purbandini

et al. 2019

Cybernetics and Information Technologies

View full text Add to dashboard Cite

show abstract

“…They removed the records that contain a lot of missing data and imputed those contain few ones. In (Borah and Nath, 2018), the authors firstly removed the PROTIME attribute and then removed all observations with more than 25% missing values and finally they imputed the rest with the mode value of the respective attribute. They used a rare association rule to build a medical diagnosis system by studying the infrequent correlations between dissimilar patient characteristics and diseases.…”

Section: Hepatitis Dataset and Literature Reviewmentioning

confidence: 99%

Diagnosis of Hepatitis Disease with Logistic Regression and Artificial Neural Networks

Elsayad¹,

Nassef²,

Al‐Dhaifallah³

2020

Journal of Computer Science

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Hepatitis C refers to the inflammatory state of the liver caused by viruses, bacteria, fungi, and exposure to toxins such as alcohol and selfimmunity. The diagnosis requires investigating many laboratory tests and comparing the results to those of the former patients with the same conditions. This study presents the results of our experiments to build a hybrid system that combines both neural networks and logistic regression for the diagnosing of the hepatitis dataset using clinical and laboratory test results. The first experiment compared the performances of Multilayer Perceptual Neural Networks (MLPNN) and Radial Basis Function Neural Network (RBFNN) versus the conventional and stepwise Logistic Regression (LR) algorithms, where the results demonstrated the ability of neural networks to deliver better performance than LR models. In the second experiment, the features selected by backward and forward LR models have been evaluated for the improvement of the performances of MLPNN and RBFNN models. The hepatitis dataset was downloaded from the machine-learning repository by the University of California at Ervine. Missing values have been imputed with a separate Classification and Regression Tree (C&RT) for each attribute. Classification models have been evaluated in terms of statistical accuracy, specificity, sensitivity, F1-score and the Area Under the Receiver Operating Characteristic Curve (AUCROC). Experimental results showed that the performances of neural network models have been improved when employing stepwise LR models to select only the predictive attributes. The hybrid system which combined both backward stepwise LR for attribute selection and MLPNN for classification has outperformed other systems in the diagnosis of the hepatitis dataset with 0.973 AUCROC for the training subset and 0.886 for the test one.

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“…The paradigm of frequent pattern mining considers the rare patterns to be of least importance, demanding their removal during the phase of pattern generation. Of late, it has been identified that the rare patterns are significant for many application domains [7,8,[10][11][12]. A pattern is considered to be rare if its support value lies below the pre-defined support value.…”

Section: Literature Reviewmentioning

confidence: 99%

Tree based frequent and rare pattern mining techniques: a comprehensive structural and empirical analysis

Borah

Nath

2019

SN Appl. Sci.

Self Cite

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Pattern mining has emerged as a compelling field of research over the years due to its extensive popularity in several application domains. Researchers in this field have contributed ample endeavors, encompassing different issues encountered during pattern generation. The patterns are either extracted based on a level-wise exploration of the search space or by employing efficient tree data structures. Tree based approaches show remarkable performance over the former ones on various grounds and are therefore regarded as the most prominent techniques of pattern mining. A precise and impartial analysis of these eminent techniques is necessary to widen the scope of effective pattern mining using the notion of tree data structures. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental tree based pattern mining techniques through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced tree based pattern mining techniques in four categories and an analytical comparison of these techniques using benchmark real and synthetic datasets. Through this empirical study, an endeavor has been made to enable the researchers identify the factors affecting the performance of the most well-known techniques of pattern mining: the tree based approaches.

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Identifying risk factors for adverse diseases using dynamic rare association rule mining

Cited by 69 publications

References 36 publications

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Diagnosis of Hepatitis Disease with Logistic Regression and Artificial Neural Networks

Tree based frequent and rare pattern mining techniques: a comprehensive structural and empirical analysis

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