Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set

Akbaş, Kübra; Kivrak, Mehmet; Arslan, Ahmet; Çolak, Cemil

doi:10.1109/idap.2019.8875977

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Metode Certainty Factor juga digunakan untuk menganalisis penilaian aturan asosiasi pada kumpulan data jantung yang menghasilkan 55 kriteria yang mendukung dengan support sebesar 15% dan confidence 95% untuk dapat dipertimbangkan lagi, Dengan adanya Certainty Factor 55 kriteria tersebut dapat dipilih lagi kriteria yang paling tepat dan didapatkan 26 kriteria berdasarkan faktor kepastian yang ada. Sehingga dengan adanya Certainty Factor akan diperoleh aturan yang paling tepat dalam data mining [11].…”

Section: Pendahuluanunclassified

Sistem Pakar Menggunakan Metode Certainty Factor untuk Mengidentifikasi Penyakit pada Hewan Peliharaan

Magfira

Nurcahyo

2020

jidt

View full text Add to dashboard Cite

Large domesticated types of ruminants such as goats, buffalo and cows are animals that are commonly kept and used as food sources and as assistants to human work in rural areas. Knowledge about pets, especially animal health, is something owners really need to keep their pets healthy. The owner's lack of knowledge about diseases and early handling of diseases in pets and the difficulty of seeing a veterinarian in urgent situations prevent pets from getting proper first aid. This study aims to identify the types of diseases suffered by pets based on the symptoms experienced by pets precisely. The method used is themethod Certainty Factor to accommodate the uncertainty of an expert's thinking on 12 diseases and 47 disease symptoms in pets. The results of this study can identify diseases in pets and produce certainty values for the types of diseases in the form of diseases suffered by pets. So that this research can be a reference in identifying diseases in pets and providing knowledge to owners about first aid and disease management in pets.

show abstract

Section: Pendahuluanunclassified

Sistem Pakar Menggunakan Metode Certainty Factor untuk Mengidentifikasi Penyakit pada Hewan Peliharaan

Magfira

Nurcahyo

2020

jidt

View full text Add to dashboard Cite

show abstract

“…Thanks to these rules, connection of various variables can be identified in medical information and that situation helps to reach diagnosis (8). Algorithms such as apriori, eclat and FP-growth algorithms are used in association rules mining and apriori algorithm is the most commonly used among these algorithms (6,9). For generating stronger rules by interest measures such as lift, conviction, certainty factor, cosine, coefficient of correlation (phi) and mutual information, this study intends to constitute strong association rules by using the studied data of the patients suffering from acute myocardial infarction.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Association Rule Mining Using Interest Measures on the Gene Data

et al. 2022

Self Cite

View full text Add to dashboard Cite

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms. Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim of obtaining stronger rules. In this study For reaching stronger rules, interest measures lift, conviction, certainty factor, cosine, phi and mutual information are applied. Results: In this study, 108 rules were obtained. The proposed interest measures were implemented to reach stronger rules and as a result 29 of the rules were qualified as strong. Conclusion: As a result, stronger rules have been obtained with the use of interest measures in the clinical decision making process. Thanks to the strong rules obtained, it will facilitate the patient profile determination and clinical decision-making process of AMI patients.

show abstract

Recognition of Heart Disease Based on Primitive Parameters Using Machine Learning Model

Digumarthi,

Gayathri,

Pitchai

2023

2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set

Cited by 6 publications

References 6 publications

Sistem Pakar Menggunakan Metode Certainty Factor untuk Mengidentifikasi Penyakit pada Hewan Peliharaan

Sistem Pakar Menggunakan Metode Certainty Factor untuk Mengidentifikasi Penyakit pada Hewan Peliharaan

Assessment of Association Rule Mining Using Interest Measures on the Gene Data

Recognition of Heart Disease Based on Primitive Parameters Using Machine Learning Model

Contact Info

Product

Resources

About