Performance Analysis of Data Mining Methods  for Sexually Transmitted Disease Classification

Yuliastuti, Gusti Eka; Alfiyatin, Adyan Nur; Rizki, Agung Mustika; Hamdianah, Andi; Taufiq, Hilman; Mahmudy, Wayan Firdaus

doi:10.11591/ijece.v8i5.pp3933-3939

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The dataset composes of detailed records of students who currently enroll. A synthetic dataset with 16 attributes and 480 instances also has been employed by [6] for the mining process. Especially, how to apply elearning data analytics to help disabled students has been presented by [7].…”

Section: E-learning Datasetmentioning

confidence: 99%

Proposed classification for eLearning data analytics with MOA

Jittawiriyanukoon¹

2019

IJECE

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<span>Elearning education has developed a crucial factor in the educational organization. With the situation of declining student size, elearning has to offer more cross-departmental and multi-disciplinary courses for individual needs to go over “one-size-fits-all” traditional model. Elearning data analytics which has not been professionally classified cannot produce reliable results. Classifications for elearning data help comfort the accuracy of outcomes and reducible pre-processing time. This research proposes a practical model for individual learning and personality. The proposed model based on data from the LMS classifies both the student preferences and personalities. The model helps design future curricula to suit student personalities, which intangibly assists them to be efficient in the study practice. Performance of the proposed classification is evaluated by using MOA software. It outperforms and improves the accuracy of complex elearning datasets. Besides, the results indicate an achievement in the students' study time after applying the association rule model on the elearning.</span>

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Section: E-learning Datasetmentioning

confidence: 99%

Proposed classification for eLearning data analytics with MOA

Jittawiriyanukoon¹

2019

IJECE

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“…In the context of STIs, some works found in the literature have used machinelearning models to predict potential cases [9] [10] [11], including syphilis. Nevertheless, these works do not focus exclusively on syphilis, they also target other STIs.…”

Section: Introductionmentioning

confidence: 99%

Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil

Leite,

Rocha,

Teixeira

et al. 2024

Preprint

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Syphilis can be transmitted congenitally and may cause serious consequences forthe child if not treated. The Programa Mãe Coruja Pernambucana (PMCP) is a brazilian public health program that helps pregnant women and saves data that can be used for prediction of potential congenital syphilis cases, through machine learning models. Only one work was found that predicts congenital syphilis through machine learning. This research uses a different methodology that evaluates undersampling in prediction. Random Undersampling, UnderSampling Based on Clustering (SBC) and NearMiss were used. The data was preprocessed and undersampling applied, generating different balanced datasets to train and test different machine learning models and different metrics for evaluation. Undersampling discarded data evaluation and analysis of distribution of the best attributes were applied to evaluate undersampling in best models. In models results, NearMiss trained models had high metrics, and very low in the discarded data. SBC models had smaller metrics, and in the discarded data went lower. Random Undersampling models had the lowest metrics, however in the discarded data showed similar results. The distribution of best attributes of NearMiss models were not similar to the original, contrary to Random Undersampling and SBC. NearMiss models had best results in the models, through this work evaluation showed that they cannot generalize the PMCP data and have not a representative distribution of the original data. Random Undersampling models had the lowest metrics but showed consistency through the evaluations, and thus are recommended for the congenital syphilis prediction.

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AI applications in prevalent diseases and disorders

Catania¹

2021

Foundations of Artificial Intelligence in Healthcare and Bioscience

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Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Cited by 5 publications

References 23 publications

Proposed classification for eLearning data analytics with MOA

Proposed classification for eLearning data analytics with MOA

Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil

AI applications in prevalent diseases and disorders

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