K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining

Dwi Normawati, Dewi Pramudi Ismi

Abstract


Coronary heart disease occurs when atheroclerosis inhibits blood flow to the heart muscle in the coronary arteries. This disease is often the cause of human death. The method for diagnosing coronary heart disease that is often a doctor's referral is coronary angiography, but it is invasive, expensive, and high-risk. This study aims to analyze the effect of k-Fold Cross-Validation (CV) on the dataset to create features based on the rules used to diagnose coronary heart disease. This study uses the Cleveland heart disease dataset, where feature selection is performed using a medical expert-based method (MFS) and a computer-based method, Variable Precision Rough Set (VPRS). Evaluation of the classification performance using the k-fold 10-fold, 5-fold and 3-fold methods. The results showed the number of different attribute selection results in each fold, both for the VPRS and MFS methods, with the highest accuracy score in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3.

Keywords


Coronary Heart Disease; Feature Selection; K-Fold; VPRS; Cross-validation

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References


O. S. R. N.M. Segerson And D.S. Romaine, The Encyclopedia of The Heart Diseases, 2nd ed. 2010.

Arthur Selzer, Understanding Heart Disease. 1992.

S. Mendis, P. Puska, and B. Norrving, “Global atlas on cardiovascular disease prevention and control,” World Heal. Organ., pp. 2–14, 2011.

M. Kumari and S. Godara, “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction,” Ijcst, vol. 4333, no. 2229, pp. 304–308, 2011.

B. Phibbs, A Basic Guide to Heart Disease. Philadelphia: Lippincott Williams & Wilkins, 2007.

R. P. Sanjaya, “Deteksi Penyakit Jantung Koroner Menggunakan Model Variable Precision Rough Set dan Logika Fuzzy,” University of Gadjah Mada, 2014.

B. . Tripathy, D. . Acharjya, and V. Cynthya, “A Framework for Intelligent Medical Diagnosis Using Rough Set with Formal Concept Analysis,” Int. J. Artif. Intell. Appl., vol. 2, no. 2, pp. 45–66, 2011.

D. Normawati, “Diagnosis penyakit jantung koroner menggunakan penambangan data berbasis variable precision rough set (vprs) dan repeated incremental pruning to produce error reduction (ripper),” university of gajah mada, 2015.

H. A. Nugroho, D. Normawati, N. A. Setiawan, and W. K. Z. Oktoeberza, “Rule-Based Data Mining for Diagnosis of Coronary Heart Disease,” JTEC, vol. 9, no. 3, pp. 93–97, 2017.

D. Normawati et al., “Data Berbasis Variable Precision Rough Set ( Vprs ) Untuk Diagnosis Penyakit Jantung,” vol. 3, no. 2, 2017.

D. Normawati and S. Winarti, “Feature selection with combination classifier use rules-based data mining for diagnosis of coronary heart disease,” Proceeding 2018 12th Int. Conf. Telecommun. Syst. Serv. Appl. TSSA 2018, pp. 2–7, 2019.

S. Iii, “K -Fold Cross-Validation,” 2009.

UCI, “Heart Disease Dataset,” 2017. [Online]. Available: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/. [Accessed: 24-Mar-2017].

Dwi Wahyu Prabowo, “SELEKSI FITUR BERBASIS KOMPUTER UNTUK DIAGNOSIS PENYAKIT JANTUNG KORONER,” University of Gadjah Mada, 2014.

Fathul Ihsan and Noor Akhmad Setiawan, “Perbandingan Metode Diskretisasi Untuk Berbagai Macam Algoritma Machine Learning,” University of Gadjah Mada, 2013.

T. Herawan, W. Maseri, W. Mohd, and A. Noraziah, “Applying Variable Precision Rough Set for Clustering Diabetics Dataset.”

W. Ziarko, “Probabilistic Decision Tables in the Variable Precision Rough Set Model,” Comput. Intell., vol. 17, no. 3, pp. 593–603, 2001.

C. T. Su and J. H. Hsu, “Precision parameter in the variable precision rough sets model: An application,” Omega, vol. 34, no. 2, pp. 149–157, 2006.

Jinwei Han and Michaline Kamber, Data Mining, south asia edition : Concept and Technology. Morgan Kaufmann, 2006.




DOI: https://doi.org/10.31763/simple.v1i2.3

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SIGNAL AND IMAGE PROCESSING LETTERS (SIMPLE)

ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website : https://simple.ascee.org/index.php/simple/
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Email 2 : azhari@ascee.org


 

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