A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Dedi Saputra; Windi Irmayani; Deasy Purwaningtias; Juniato Sidauruk; Burcu Gurbuz

doi:10.25008/ijadis.v2i2.1221

Authors

Dedi Saputra Bina Sarana Informatika of University, Indonesia
Windi Irmayani Bina Sarana Informatika of University, Indonesia
Deasy Purwaningtias Bina Sarana Informatika of University, Indonesia
Juniato Sidauruk Bina Sarana Informatika of University, Indonesia
Burcu Gurbuz Johannes Gutenberg-University of Mainz, Germany

DOI:

https://doi.org/10.25008/ijadis.v2i2.1221

Keywords:

Heart Disease, Classification Algorithms, Optimization, Particle Swarm Optimization (PSO), Naive Bayes, Support Vector Machine

Abstract

Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease.

Downloads

Download data is not yet available.

References

S. I. Novichasari and I. S. Wibisono, "Particle Swarm Optimization For Improved Accuracy of Disease Diagnosis," J. Appl. Intell. Syst., vol. 5, no. 2, pp. 57-68, 2020.

R. Wajhillah, "Optimasi Algoritma Klasifikasi C4. 5 Berbasis Particle Swarm Optimization Untuk Prediksi Penyakit Jantung," Swabumi, vol. 1, no. 1, pp. 26-36, 2014. https://doi.org/10.33369/pseudocode.1.1.11-14

F. Babi?, J. Olejár, Z. Vantová, and J. Parali?, "Predictive and descriptive analysis for heart disease diagnosis," in 2017 federated conference on computer science and information systems (fedcsis), 2017, pp. 155-163. https://doi.org/10.15439/2017F219

M. Martiningsih and A. Haris, "Risiko Penyakit Kardiovaskuler pada Peserta Program Pengelolaan Penyakit Kronis (Prolanis) di Puskesmas Kota Bima: Korelasinya dengan Ankle Brachial Index dan Obesitas," J. Keperawatan Indones., vol. 22, no. 3, pp. 200-208, 2019. https://doi.org/10.7454/jki.v22i3.880

B. A. Tama, S. Im, and S. Lee, "Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble," Biomed Res. Int., vol. 2020, 2020. https://doi.org/10.1155/2020/9816142

P2PTM Kemenkes RI, "Hari Jantung Sedunia (HJS) Tahun 2019?: Jantung Sehat, SDM Unggul," 2018.

R. I. Kemenkes, "Hasil utama RISKESDAS 2018," Online) http//www. depkes. go. id/resources/download/info-terkini/materi_rakorpop_2018/Hasil% 20Riskesdas, vol. 202018, 2018.

P. S. Patel, J. Undavia, and D. Bhatti, "Master Course Selection Prediction Model Using Modify Hybrid Neuro-fuzzy Inference System," ICTACT J. Soft Comput., vol. 11, no. 1, pp. 2205-2212, 2020.

S. Saha and D. Nandi, "Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison," Int. J. Comput. Appl, vol. 129, no. 7, pp. 36-41, 2015. https://doi.org/10.5120/ijca2015906891

R. Rachmat and S. Suhartono, "Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital," Bull. Comput. Sci. Electr. Eng., vol. 1, no. 2 SE-Articles, pp. 80-86, Aug. 2020, doi: 10.25008/bcsee.v1i2.8. https://doi.org/10.25008/bcsee.v1i2.8

S. B. Sakri, N. B. A. Rashid, and Z. M. Zain, "Particle swarm optimization feature selection for breast cancer recurrence prediction," IEEE Access, vol. 6, pp. 29637-29647, 2018. https://doi.org/10.1109/ACCESS.2018.2843443

N. A. Widiastuti, S. Santosa, and C. Supriyanto, "Algoritma Klasifikasi data mining naïve bayes berbasis Particle Swarm Optimization untuk deteksi penyakit jantung," Pseudocode, vol. 1, no. 1, pp. 11-14, 2014. https://doi.org/10.33369/pseudocode.1.1.11-14

N. Iriadi and N. Nuraeni, "kajian penerapan metode klasifikasi data mining algoritma C4. 5 untuk prediksi kelayakan kredit pada bank mayapada jakarta," J. Tek. Komput., vol. 2, no. 1, pp. 132-137, 2016.

W. Bourequat and H. Mourad, "Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine," Int. J. Adv. Data Inf. Syst., vol. 2, no. 1 SE-, pp. 36-44, Apr. 2021, doi: 10.25008/ijadis.v2i1.1216. https://doi.org/10.25008/ijadis.v2i1.1216

S. Suthaharan, "Support vector machine," in Machine learning models and algorithms for big data classification, Springer, 2016, pp. 207-235. https://doi.org/10.1007/978-1-4899-7641-3_9

D. Saputra, F. Akbar, and A. Rahman, "Decision Support System For Providing Customer Reward Using Profile Matching Method: A Case Study at PT. Atlas Jakarta," Bull. Comput. Sci. Electr. Eng., vol. 2, no. 1, pp. 28-37, 2021.

D. Nofriansyah, S. Kom, and M. Kom, Konsep Data Mining Vs Sistem Pendukung Keputusan. Deepublish, 2015.

Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika Bandung, 2017.

T. Kristanto and R. Arief, "Analisa Data Mining Metode Fuzzy Untuk Customer Relationship Management Pada Perusahaan Tour & Travel," SESINDO 2013, vol. 2013, 2013.

R. Annisa, "ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI PENDERITA PENYAKIT JANTUNG," JTIK (Jurnal Tek. Inform. Kaputama), vol. 3, no. 1, pp. 22-28, 2019.

A. Muzakir and R. A. Wulandari, "Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree," Sci. J. Informatics, vol. 3, no. 1, pp. 19-26, 2016. https://doi.org/10.15294/sji.v3i1.4610

D. Saputra, W. S. Dharmawan, M. Wahyudi, W. Irmayani, J. Sidauruk, and Martias, "Performance Comparison and Optimized Algorithm Classification," in Journal of Physics: Conference Series, 2020, vol. 1641, no. 1, pp. 12087-12093, doi: https://doi.org/10.1088/1742-6596/1641/1/012087

Y. I. Kurniawan, "Perbandingan Algoritma Naive Bayes dan C. 45 Dalam Klasifikasi Data Mining," J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 455-464, 2018. https://doi.org/10.25126/jtiik.201854803

D. Sartika and D. I. Sensuse, "Perbandingan algoritma klasifikasi Naive Bayes, Nearest Neighbour, dan Decision Tree pada studi kasus pengambilan keputusan pemilihan pola pakaian," JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 3, no. 2, pp. 151-161, 2017.

A. Supriyatna and W. P. Mustika, "Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil," J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 2, no. 2, pp. 152-161, 2018. https://doi.org/10.30645/j-sakti.v2i2.78

P. A. Rahayuningsih, R. Maulana, W. Irmayani, D. Saputra, and D. Purwaningtias, "Feature Dependent Naïve Bayes For Network Intrusion Detection System," J. Phys. Conf. Ser., vol. 1641, p. 12023, 2020, doi: https://doi.org/10.1088/1742-6596/1641/1/012023

I. Aydin, M. Karakose, and E. Akin, "A multi-objective artificial immune algorithm for parameter optimization in support vector machine," Appl. Soft Comput., vol. 11, no. 1, pp. 120-129, 2011. https://doi.org/10.1016/j.asoc.2009.11.003

Y. Yin, D. Han, and Z. Cai, "Explore Data Classification Algorithm Based on SVM and PSO for Education Decision," J. Converg. Inf. Technol., vol. 6, no. 10, pp. 122-128, 2011, doi: https://doi.org/10.4156/jcit.vol6.issue10.16

R. Maulana, P. A. Rahayuningsih, W. Irmayani, D. Saputra, and W. E. Jayanti, "Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain," J. Phys. Conf. Ser., vol. 1641, p. 12060, 2020, doi: https://doi.org/10.1088/1742-6596/1641/1/012060

H. Yin, X. Jiao, Y. Chai, and B. Fang, "Scene classification based on single-layer SAE and SVM," Expert Syst. Appl., vol. 42, no. 7, pp. 3368-3380, 2015. https://doi.org/10.1016/j.eswa.2014.11.069

G. Li, J. You, and X. Liu, "Support vector machine (SVM) based prestack AVO inversion and its applications," J. Appl. Geophys., vol. 120, pp. 60-68, 2015. https://doi.org/10.1016/j.jappgeo.2015.06.009

P. A. Octaviani, Y. Wilandari, and D. Ispriyanti, "Penerapan Metode Klasifikasi Support Vector Machine (SVM) pada Data Akreditasi Sekolah Dasar (SD) di Kabupaten Magelang," J. Gaussian, vol. 3, no. 4, pp. 811-820, 2014.

E. Prasetyo, "Data mining konsep dan aplikasi menggunakan matlab," Yogyakarta Andi, 2012.

T.-J. Hsieh, H.-F. Hsiao, and W.-C. Yeh, "Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm," Neurocomputing, vol. 82, pp. 196-206, 2012. https://doi.org/10.1016/j.neucom.2011.11.020

C.-L. Huang and J.-F. Dun, "A distributed PSO-SVM hybrid system with feature selection and parameter optimization," Appl. Soft Comput., vol. 8, no. 4, pp. 1381-1391, 2008. https://doi.org/10.1016/j.asoc.2007.10.007

F. Zhao, Y. Liu, C. Zhang, and J. Wang, "A self-adaptive harmony PSO search algorithm and its performance analysis," Expert Syst. Appl., vol. 42, no. 21, pp. 7436-7455, 2015. https://doi.org/10.1016/j.eswa.2015.05.035

L.-Y. Hsu et al., "Mutual funds trading strategy based on particle swarm optimization," Expert Syst. Appl., vol. 38, no. 6, pp. 7582-7602, 2011. https://doi.org/10.1016/j.eswa.2010.12.105

Z. R. . Tembusai, H. . Mawengkang, and M. . Zarlis, "K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification ", Int. J. Adv. Data Inf. Syst., vol. 2, no. 1, pp. 1-8, Jan. 2021. https://doi.org/10.25008/ijadis.v2i1.1204