Analisis Dan Prediksi Penyakit Jantung Koroner Di Kota Ambon Menggunakan Jaringan Saraf Tiruan

Rahakbauw, Dorteus L.; Lembang, Ferry Kondo; Taihuttu, Yuniasih Mulyani Jubeliene

doi:10.30598/barekengvol10iss2pp97-105

Cited by 1 publication

(2 citation statements)

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“…Data on Chol attribute should be converted into categories as label data. Chol attribute could be categorized into 3 labels very high(0) : > 200 mg / dl, High(1): 160-200 mg / dl, Normal (2): <160 mg / dl (Rahakbauw et al, 2016) . For Slope attribute, it was used to represent the Slope of the ST segment at (peak).…”

Section: Attributes With More Than 1% Missing Datamentioning

confidence: 99%

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Handling Missing Data Using Combination of Deletion Technique, Mean, Mode and Artificial Neural Network Imputation for Heart Disease Dataset

Desiani

Dewi

Fauza

et al. 2021

Sci. Technol. Indones

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The University of California Irvine Heart disease dataset had missing data on several attributes. The missing data can loss the important information of the attributes, but it cannot be deleted immediately on dataset. To handle missing data, there are several ways including deletion, imputation by mean, mode, or with prediction methods. In this study, the missing data were handled by deletion technique if the attribute had more than 70% missing data. Otherwise, it were handled by mean and mode method to impute missing data that had missing data less or equal 1%. The artificial neural network was used to handle the attribute that had missing data more than 1%. The results of the techniques and methods used to handle missing data were measured based on the performance results of the classification method on data that has been handled the problem of missing data. In this study the classification method used is Artificial Neural Network, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. The performance results of classification methods without handling missing data were compared with the performance results of classification methods after imputation missing data on dataset for accuracy, sensitivity, specificity and ROC. In addition, the comparison of the Mean Squared Error results was also used to see how close the predicted label in the classification was to the original label. The lowest Mean Squared Error wasobtained by Artificial Neural Network, which means that the Artificial Neural Network worked very well on dataset that has been handled missing data compared to other methods. The result of accuracy, specificity, sensitivity in each classification method showed that imputation missing data could increase the performance of classification, especially for the Artificial Neural Network method.

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Section: Attributes With More Than 1% Missing Datamentioning

confidence: 99%

“…That is used to determine the pattern of factors that a ect a heart disease. Heart disease is one of the biggest causes of death in the world (Rahakbauw et al, 2016) . Based on research that has been conducted by Stewart et al (2017) , nearly one billion people worldwide su er strokes caused by hypertension and heart attacks.…”

Section: Introductionmentioning

confidence: 99%