Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm

Basha, Murtaza Saadique

doi:10.1007/978-981-16-3153-5_42

Cited by 6 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These algorithms are used to build a robust prediction model that could improve the prediction accuracy. Finally, Basha [21] provided a cardiovascular prediction system that combines the K-Nearest Neighbour (KNN) algorithm with a GA to extract strong association rules for facilitating the decision-making process. First, the proposed system extracts association rules using KNN algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This redundancy makes not only the rule-set unnecessarily large but also makes the decision-making process more complex and ineffective. In contrast, approaches using machine learning algorithms [19][20][21] can overcome this drawback to avoid rule redundancy. Furthermore, certain approaches derive the rule generation process at design time by offline mining.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A further drawback is that a human intervention is needed in certain approaches to validate and manage generated rules. None of the above-described approaches, apart from [21], support the rule optimization with a target of getting the wellperformed rules. Along the similar line, we aim to close gaps within the related works by proposing a component that relies on automatically enriching a rule knowledge base with new rules discovered at runtime to deal with dynamic environments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To answer the RQ.3, in this experiment, we show the effectiveness of our component against three state-of-the-art approaches, including Zulkernain et al [19], Sarker et al [20] and Basha et al [21]. For this purpose, the machine learning algorithms proposed in selected state-of-the-art approaches were implemented using the candidate datasets.…”

Section: Accuracy Comparison With State-of-the-artmentioning

confidence: 99%

See 3 more Smart Citations

Decision-making Improvement in Dynamic Environments Using Machine Learning

Jabla

Khemaja

Faïz

2022

J. Hum. Earth Future

View full text Add to dashboard Cite

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

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Accuracy Comparison With State-of-the-artmentioning

confidence: 99%

See 2 more Smart Citations

Decision-making Improvement in Dynamic Environments Using Machine Learning

Jabla

Khemaja

Faïz

2022

J. Hum. Earth Future

View full text Add to dashboard Cite

show abstract

“…Basha [Basha, 2021] provided a cardiovascular prediction system that combines the traditional K-Nearest Neighbor (KNN) algorithm with a Genetic Algorithm (GA) to extract strong association rules for facilitating the decision-making process. First, the proposed system extracts association rules using KNN algorithm.…”

Section: Rule Learning Approaches Using Machine Learning Algorithmsmentioning

confidence: 99%

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

Jabla¹

View full text Add to dashboard Cite

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.

show abstract

AC.Rank_A : Rule Ranking Method via Aggregation of Objective Measures for Associative Classifiers

Dall’Agnol,

Carvalho

2024

IEEE Access

View full text Add to dashboard Cite

Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm

Cited by 6 publications

References 11 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

AC.Rank_A : Rule Ranking Method via Aggregation of Objective Measures for Associative Classifiers

Contact Info

Product

Resources

About

Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm

Cited by 6 publications

References 11 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

AC.RankA : Rule Ranking Method via Aggregation of Objective Measures for Associative Classifiers

Contact Info

Product

Resources

About

AC.Rank_A : Rule Ranking Method via Aggregation of Objective Measures for Associative Classifiers