Peningkatan Akurasi Klasifikasi Backpropagation Menggunakan Artificial Bee Colony dan K-NN Pada Penyakit Jantung

 (*)Pandito Dewa Putra Mail (Universitas Sriwijaya, Palembang, Indonesia)
 Sukemi Sukemi (Universitas Sriwijaya, Palembang, Indonesia)
 Dian Palupi Rini (Universitas Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: December 5, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2634

Abstract

Heart disease has ranked as the leading cause of death in the world, accounting for around 17.3 million deaths per year with some causes, as high blood pressure, diabetes, cholesterol fluctuation, fatigue, and some others which is collected on dataset. Heart disease dataset that was applied is cleveland heart disease with fourteen (14) data atribute. The method that was applied in this research was using Backpropagation algorithm on heart disease classifying, where will be combined Artificial Bee Colony and k-Nearest Neighbor algorithm for features or atribute choose due to this technique can increase classifier model accuracy which is produced as much as 94,23%.

Keywords


Heart Disease; Cleveland; Backpropagation; Artificial Bee Colony; K-NN

Full Text:

PDF


Article Metrics

Abstract View: 43 times | PDF View: 6 times

References

K. M. Almustafa, “Prediction of heart disease and classifiers’ sensitivity analysis,” BMC Bioinformatics, vol. 21, no. 1, pp. 1–18, 2020, doi: 10.1186/s12859-020-03626-y.

I. Tougui, A. Jilbab, and J. El Mhamdi, “Heart disease classification using data mining tools and machine learning techniques,” Health Technol. (Berl)., vol. 10, no. 5, pp. 1137–1144, 2020, doi: 10.1007/s12553-020-00438-1.

P. Sharma and K. Saxena, “Application of fuzzy logic and genetic algorithm in heart disease risk level prediction,” Int. J. Syst. Assur. Eng. Manag., vol. 8, pp. 1109–1125, 2017, doi: 10.1007/s13198-017-0578-8.

R. Chadha and S. Mayank, “Prediction of heart disease using data mining techniques,” CSI Trans. ICT, vol. 4, no. 2–4, pp. 193–198, 2016, doi: 10.1007/s40012-016-0121-0.

G. T. Reddy, M. P. K. Reddy, K. Lakshmanna, D. S. Rajput, R. Kaluri, and G. Srivastava, “Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis,” Evol. Intell., vol. 13, no. 2, pp. 185–196, 2020, doi: 10.1007/s12065-019-00327-1.

S. M. S. Shah, F. A. Shah, S. A. Hussain, and S. Batool, “Support Vector Machines-based Heart Disease Diagnosis using Feature Subset, Wrapping Selection and Extraction Methods,” Comput. Electr. Eng., vol. 84, p. 106628, 2020, doi: 10.1016/j.compeleceng.2020.106628.

T. Vivekanandan and N. C. Sriman Narayana Iyengar, “Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease,” Comput. Biol. Med., vol. 90, no. April, pp. 125–136, 2017, doi: 10.1016/j.compbiomed.2017.09.011.

V. Chaurasia, “Early Prediction of Heart Diseases Using Data Mining,” Caribb. J. Sci. Technol., vol. 1, pp. 208–217, 2013.

S. Pouriyeh, S. Vahid, G. Sannino, G. De Pietro, H. Arabnia, and J. Gutierrez, “A comprehensive investigation and comparison of Machine Learning Techniques in the domain of heart disease,” Proc. - IEEE Symp. Comput. Commun., no. Iscc, pp. 204–207, 2017, doi: 10.1109/ISCC.2017.8024530.

N. Khateeb and M. Usman, “Efficient heart disease prediction system using K-nearest neighbor classification technique,” ACM Int. Conf. Proceeding Ser., pp. 21–26, 2017, doi: 10.1145/3175684.3175703.

T. R. Stella Mary and S. Sebastian, “Predicting heart ailment in patients with varying number of features using data mining techniques,” Int. J. Electr. Comput. Eng., vol. 9, no. 4, pp. 2675–2681, 2019, doi: 10.11591/ijece.v9i4.pp2675-2681.

L. Verma, S. Srivastava, and P. C. Negi, “A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data,” J. Med. Syst., vol. 40, no. 7, 2016, doi: 10.1007/s10916-016-0536-z.

S. Iftikhar, K. Fatima, A. Rehman, A. S. Almazyad, and T. Saba, “An evolution based hybrid approach for heart diseases classification and associated risk factors identification,” Biomed. Res., vol. 28, no. 8, pp. 3451–3455, 2017.

S. D. Desai, S. Giraddi, P. Narayankar, N. R. Pudakalakatti, and S. Sulegaon, “Back-propagation neural network versus logistic regression in heart disease classification,” Adv. Intell. Syst. Comput., vol. 702, no. July, pp. 133–144, 2019, doi: 10.1007/978-981-13-0680-8_13.

N. Leema, H. K. Nehemiah, and A. Kannan, “Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets,” Appl. Soft Comput. J., vol. 49, pp. 834–844, 2016, doi: 10.1016/j.asoc.2016.08.001.

B. Subanya and R. R. Rajalaxmi, “Feature selection using artificial bee colony for cardiovascular disease classification,” 2014 Int. Conf. Electron. Commun. Syst. ICECS 2014, 2014, doi: 10.1109/ECS.2014.6892729.

T. Prasartvit, A. Banharnsakun, B. Kaewkamnerdpong, and T. Achalakul, “Reducing bioinformatics data dimension with ABC-kNN,” Neurocomputing, vol. 116, pp. 367–381, 2013, doi: 10.1016/j.neucom.2012.01.045.

D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 652–657, 2011, doi: 10.1016/j.asoc.2009.12.025.

F. S. Gharehchopogh, S. R. Khaze, and I. Maleki, “A new approach in bloggers classification with hybrid of K-nearest neighbor and artificial neural network algorithms,” Indian J. Sci. Technol., vol. 8, no. 3, pp. 237–246, 2015, doi: 10.17485/ijst/2015/v8i3/59570.

F. Ortega-Zamorano, J. M. Jerez, G. E. Juárez, and L. Franco, “FPGA Implementation of Neurocomputational Models: Comparison Between Standard Back-Propagation and C-Mantec Constructive Algorithm,” Neural Process. Lett., vol. 46, no. 3, pp. 899–914, 2017, doi: 10.1007/s11063-017-9655-x.

M. G. Feshki and O. S. Shijani, “Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network,” 2016 Artif. Intell. Robot. IRANOPEN 2016, pp. 48–53, 2016, doi: 10.1109/RIOS.2016.7529489.

S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.

T. Karayilan and Ö. Kiliç, “Prediction of Heart disease using neural network,” 2nd Int. Conf. Comput. Sci. Eng. UBMK 2017, pp. 719–723, 2017, doi: 10.1109/UBMK.2017.8093512.

S. Yadav and S. Shukla, “Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification,” Proc. - 6th Int. Adv. Comput. Conf. IACC 2016, no. Cv, pp. 78–83, 2016, doi: 10.1109/IACC.2016.25.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Peningkatan Akurasi Klasifikasi Backpropagation Menggunakan Artificial Bee Colony dan K-NN Pada Penyakit Jantung

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.