Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for Predicting Obstructive Sleep Apnea

Sim, Doreen Ying Ying; Teh, Chee Siong; Ismail, Ahmad Izuanuddin

doi:10.1166/asl.2017.10335

Cited by 7 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A good example would be if customers were looking to purchase milk, they were likely to purchase bread on the same trip to the supermarket. This approach is also widely used in the healthcare industry specifically in privacy preservation of healthcare data [ 15 ], predicting cancer associated protein interactions [ 12 ], predicting obstructive sleep apnea [ 43 ] and predicting co-diseases in Thyroid patients [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

A novel approach for heart disease prediction using strength scores with significant predictors

Yazdani

Varathan

Chiam

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features. Method This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining. Results A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease. Conclusion This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.

show abstract

Section: Related Workmentioning

confidence: 99%

A novel approach for heart disease prediction using strength scores with significant predictors

Yazdani

Varathan

Chiam

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…AA, like Apriori, serves as a strategy to discover monotonous pattern(s) from dataset(s). AA is the key to mine frequent item-sets which are to group earlier these common monotonous item-sets generated [1,3,[9][10]. AA is adopted to avoid decision trees from unnecessarily splitting too many times and thereby incurring over-fitting of trees.…”

Section: Pushed Minimum Constraints and Support Constraint Specificat...mentioning

confidence: 99%

“…in notation this is expressed as rr  U rr' and rr  T rr'. Definition 3 (Pushed Minimum Support) [1,9,14]: Let Pminsup be a function from the schema of schema enumerated tree T to [0..1] satisfying the following:…”

Section: If Sup(fis')/sup(fis'-fis)mentioning

confidence: 99%

“…(2) Uniform-minimum-support-like: For a schema s and its generating schemas s1 and s2, whenever an itemset I of s is frequent (Pminsup), so are the I1 of s1 and I2 of s2; (3) Maximal Pushed Minimum Support: Pminsup is maximal with respect to the Total Coverage and Uniform-minimum-support-like. Theorem (Pushed Minimum Support) [1,[7][8][9]14]: Consider an ancestor or parent node, p, and its offspring node, c, in any dataset where AA properties are to be contemplated. The following properties [7][8][9] 2 shows the pushing minimum support at each support constraint (SC) and the OSA attributes in each assigned bin (i.e.…”

Section: If Sup(fis')/sup(fis'-fis)mentioning

confidence: 99%

“…But, interesting rules are often not being supported by frequent itemsets. Hence, mining frequent itemsets and using association rules to construct a prediction system for Obstructive Sleep Apnea (OSA) based on the raw data collected in Malaysia is still yet to be researched upon [1][2][3][4]. Pruning techniques applied in this research mainly involve in the association rules, especially those augmented by Adaptive Apriori (AA), applied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Sim

Teh

Ismail³

2019

AMM

Self Cite

View full text Add to dashboard Cite

Boosted Association-Ruled Pruned Decision Tree (ARP-DT), the improved version of the Boosted Decision Tree algorithm, was developed by using association-ruled pre-and post-pruning techniques with referring to the pushed minimum support and minimum confidence constraints as well as the association rules applied. The novelty of the Association-Ruled pruning techniques applied mainly embark on the pre-pruning techniques through researching on the maximum number of decision tree splitting, as well as the post-pruning techniques involving subtree replacement and subtree raising. The applied association rules (ARs) augment the mining of frequent itemset (s) or interesting itemset (s) such that appropriate pre-pruning or subtree pruning techniques can be applied before AdaBoost ensemble is implemented. The ARs applied involve the Adaptive Apriori (AA) augmented rule definitions and theorem as stated in this research focuses on the characteristics of the datasets accessed so as to streamline the rule-driven pruning techniques on the Boosting algorithms developed for predicting Obstructive Sleep Apnea (OSA). There is a significant improvement in the prediction accuracies when comparing the classical boosting algorithms and Boosted ARP-DT being applied to the OSA datasets and those online databases from University of California Irvine (UCI) data repositories.

show abstract

Stop Criterion in Building Decision Trees with Bagging Method for Dispersed Data

Przybyła–Kasperek

Aning

2021

Procedia Computer Science

View full text Add to dashboard Cite

Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for Predicting Obstructive Sleep Apnea

Cited by 7 publications

References 0 publications

A novel approach for heart disease prediction using strength scores with significant predictors

A novel approach for heart disease prediction using strength scores with significant predictors

Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Stop Criterion in Building Decision Trees with Bagging Method for Dispersed Data

Contact Info

Product

Resources

About