Data Mining Apriori Algorithm for Heart Disease Prediction

doi:10.15242/ijccie.dir1116010

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…In order to evaluate heart disease, Mirmozaffari et al [16] proposed a method for the classification of various data mining methods. It has developed a particular model of different filters and methods of analysis.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Al-Taie

Saleh

Saedi

et al. 2021

IJECE

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Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Al-Taie

Saleh

Saedi

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…These innovative methodologies, which involve the comprehensive collection and analysis of data pertaining to athletes' movements, training patterns, and injury histories, are instrumental in identifying risk factors for knee injuries. This, in turn, facilitates the development of targeted interventions to mitigate such occurrences, a critical advancement in athlete health and performance [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Sarlis,

Papageorgiou,

Tjortjis

2024

Computation

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This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact on players’ performance. It employs a unique dataset to identify common NBA injuries, determine the most affected anatomical areas, and analyze how these injuries influence players’ post-recovery performance. This study’s novelty lies in its integrative approach that combines injury data with performance metrics and salary data, providing new insights into the relationship between injuries and economic and on-court performance. It investigates the periodicity and seasonality of injuries, seeking patterns related to time and external factors. Additionally, it examines the effect of specific injuries on players’ per-match analytics and performance, offering perspectives on the implications of injury rehabilitation for player performance. This paper contributes significantly to sports analytics, assisting coaches, sports medicine professionals, and team management in developing injury prevention strategies, optimizing player rotations, and creating targeted rehabilitation plans. Its findings illuminate the interplay between injuries, salaries, and performance in the NBA, aiming to enhance player welfare and the league’s overall competitiveness. With a comprehensive and sophisticated analysis, this research offers unprecedented insights into the dynamics of injuries and their long-term effects on athletes.

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Relevance of Frequent Pattern (FP)-Growth-Based Association Rules on Liver Diseases

Patel

Sivaiah

Patel

2022

Intelligent Systems

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Data Mining Apriori Algorithm for Heart Disease Prediction

Cited by 10 publications

References 3 publications

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Relevance of Frequent Pattern (FP)-Growth-Based Association Rules on Liver Diseases

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