Expert systems for self-diagnosing of eye diseases using Na&amp;#x00EF;ve Bayes

Kurniawan, Rahmad; Yantі, Novі; Nazri, Mohd Zakree Ahmad; Zulvandri,

doi:10.1109/icaicta.2014.7005925

Cited by 20 publications

(7 citation statements)

References 4 publications

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“…Naïve Bayes is widely used for classification in machine learning. Naïve Bayes also be used for a lot of problems of classification for a simpler and provide accuracy that is better than the methods of machine learning more [15]. Naïve Bayes is a simple probabilistic class that learns from training data and then predicts test data based on the highest probability [16].…”

Section: Naïve Bayesmentioning

confidence: 99%

Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Syafaah

Zulfatman

Pakaya

et al. 2021

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The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.

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Section: Naïve Bayesmentioning

confidence: 99%

Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Syafaah

Zulfatman

Pakaya

et al. 2021

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“…Accordingly, they concluded that an expert system based on a combination of Naïve Bayes and CBR can be a promising technology for eye diseases diagnosis. Naïve Bayes classifier is capable of introducing effective diagnoses for people but more improvements and enhancements were still required (Kurniawan, Yanti, & Ahmad Nazri, 2014).…”

Section: 4naive Bayes Based Classifiersmentioning

confidence: 99%

Artificial intelligence in corneal topography

Saleh

Hussein²

2019

Journal of Intelligent Systems: Theory and Applications

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The purpose of this paper is to explore the effectiveness and efficiency of various artificial intelligence (AI) techniques in extracting features from corneal topographies. A considerable number of dated and contemporary related research papers have been reviewed. The author has only checked the studies that considered developing at least one AI-based algorithm for data classification of topographic patterns. The results of this review emphasize the effectiveness and efficiency of machine learning algorithms in the clinical diagnosis of various eye refractive problems.

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“…According to related works, the zakat studies merely focus on calculation of zakat and they used a rule-based method, which is traditional inference of the expert system. A traditional inference could be possible to conclude the same classes despite having different symptoms [10]. Another related work regarding zakat is a decision support system for determining Mustahik, but it has not explained the accuracy of the method.…”

Section: Related Workmentioning

confidence: 99%

Using Bayesian Network for Determining The Recipient of Zakat in BAZNAS Pekanbaru

Akbarizan

Kurniawan

Nazri

et al. 2018

2018 2nd International Conference on Electrical Engineering and Informatics (ICon EEI)

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The National Amil-Zakat Agency (Baznas) in Pekanbaru has the function to collect and distribute zakat in Pekanbaru city. Baznas Pekanbaru should be able to determine Mustahik properly. Mustahik is a person eligible to receive zakat. The Baznas committee interviews and observes every Mustahik candidates to decide whom could be receive the zakat. Current Mustahik determination process could lead to be subjective assessment, due to large number of zakat recipient applicants and the complexity of rules in determining a Mustahik. Therefore, this study utilize artificial intelligence in determining Mustahik. The Bayesian Network method is appropriate to apply as an inference engine. Based on the experimental results, we found that Bayesian network produces a good accuracy 93.24% and effective to use in data set have an uneven class distribution. In addition, based on experiments by setting an alpha estimator's values, at 0.6 to 1.0 can increase the accuracy of a Bayesian Network to 95.95%.

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Expert systems for self-diagnosing of eye diseases using Naïve Bayes

Cited by 20 publications

References 4 publications

Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Artificial intelligence in corneal topography

Using Bayesian Network for Determining The Recipient of Zakat in BAZNAS Pekanbaru

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