Association Rules Mining for Hospital Readmission: A Case Study

Miswan, Nor Hamizah; Sulaiman, Ismat Mohd; Chan, Chee Seng; Ng, Chong Guan

doi:10.3390/math9212706

Cited by 10 publications

(6 citation statements)

References 63 publications

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“…They showed that the generated association rules could increase the overall efficiency of the recommender system. More recently, Miswan et al [16] proposed a framework of association rule mining in readmission tasks. The proposed framework consisted of two processes, namely data pre-processing and rule mining extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, a common weakness that can be found in the most of above-described approaches [7][8][9][10][11][12][13][14][15][16][17][18] stands on the use of traditional algorithms, such as Apriori and FP-Growth. This drawback is due to the narrow applicability of these algorithms and the huge number of generated association rules [22].…”

Section: Literature Reviewmentioning

confidence: 99%

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Decision-making Improvement in Dynamic Environments Using Machine Learning

Jabla

Khemaja

Faïz

2022

J. Hum. Earth Future

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The proliferation of ubiquitous computing with smartphones makes context models and information extremely rich and dynamic on account of highly dynamic environments. However, defining rules at design time may impair their efficiency and the decision-making process at runtime. Therefore, it is important to address decision-making problems leveraged by dynamic environments and context-model evolution. In this sense, a solution that could emerge is the continuous rule knowledge base evolution at runtime. In this paper, we propose a decision adaptation component that relies on generating new rules due to changes occurring around users at runtime. This component aims to support decision-making in dynamic environments and to alleviate the human effort needed to infer new rules. A case study was conducted to illustrate the implementation of the proposed component for the rule knowledge database enrichment and the decision-making improvement. Moreover, an experimental evaluation is provided to assess the effectiveness of the proposed component. The results show that this component exhibits better effectiveness than other well-known algorithms and state-of-the-art approaches. Doi: 10.28991/HEF-2022-03-01-04 Full Text: PDF

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Decision-making Improvement in Dynamic Environments Using Machine Learning

Jabla

Khemaja

Faïz

2022

J. Hum. Earth Future

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“…Thus, it is witnessed that they are definitely unsuitable for dynamic environments, where new incoming changes are received continuously at runtime and may wrongly take decisions based on rules defined a priori. Apart from these limitations, a common limitation of previous approaches was that certain of them, as [Miswan et al, 2021], require domain expert intervention to validate the generated rules.…”

Section: Rule Learning Approaches Discussionmentioning

confidence: 99%

“…• Generating a redundant and large number of rules • Generating a redundant and large number of rules [Asadianfam et al, 2020] • Generating a redundant and large number of rules • Generating a redundant and large number of rules [Miswan et al, 2021] • Generating a redundant and large number of rules…”

Section: Rule Learning Approaches Discussionmentioning

confidence: 99%

“…They showed that the generated association rules could increase the overall efficiency of the recommender system. Miswan et al [Miswan et al, 2021] proposed a framework of association rule mining in readmission tasks. The proposed framework consisted of two processes, namely data pre-processing and rule mining extraction.…”

Section: Rule Learning Approaches Using Traditional Association Rule ...mentioning

confidence: 99%

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Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware

Jabla¹

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This thesis describes research to gain insight into pervasive middleware solutions and context-aware solutions that expand their perspective from static to dynamic pervasive environments. The motivation behind this research arose from a need to reconsider and replace today's context-aware solutions with more intelligent solutions to account for dynamic environments and users' preferences changes at runtime. In this context, the end goal is to focus on offering intelligent context-aware solutions that could deal with the automatic context model evolution and new decisions generation according to context changes at runtime. To do so, in the current thesis, we illustrate a hybrid approach termed IConAS -a means of combining the practical advantages of context evolution with the decision-making adaptation. This combination leads to intelligent context-aware solutions that could reflect changes occurring in their surrounding dynamic environments at runtime. The thesis concentrates on the three important contributions as follows:• Definition of the IConAS approach that combines two main approaches. This hybrid approach aims to offer intelligent context-aware solutions through augmenting an existing middleware. The purpose of this augmentation is to support runtime and automatic context evolution and decision-making adaptation in order to reflect changes in dynamic environments;Next, I would like to extend my heartiest gratitude and thanks to my co-supervisor Prof. Maha Khemaja, who believed in me from the beginning of this thesis and who never ceased to encourage, guide and kindly nudge me forward when I doubt my ability or could not see the finish line. This thesis would not have been achievable without her constant feedback, her enormous research contributions and her emotional support. I am sincerely thankful to my external evaluators of the thesis, Prof.

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Identifying diseases symptoms and general rules using supervised and unsupervised machine learning

Sogandi

2024

Sci Rep

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The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

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Association Rules Mining for Hospital Readmission: A Case Study

Cited by 10 publications

References 63 publications

Decision-making Improvement in Dynamic Environments Using Machine Learning

Decision-making Improvement in Dynamic Environments Using Machine Learning

Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware

Identifying diseases symptoms and general rules using supervised and unsupervised machine learning

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