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Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM) | Arif | JURNAL MEDIA INFORMATIKA BUDIDARMA

Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM)

Siti Novianti Nuraini Arif, Amril Mutoi Siregar, Sutan Faisal, Ayu Ratna Juwita

Abstract


Cardiovascular disease (CVD) is a general term for disorders related to the heart, coronary arteries, and blood vessels. These diseases are most commonly caused by blocked blood vessels, either due to fat buildup or internal bleeding. According to the WHO, each year, cardiovascular diseases account for 32% of all deaths, which translates to about 17.9 million people annually. The numerous factors causing CVD make it challenging for doctors to diagnose patients who are at low or higher risk of heart attacks. A machine learning model is needed for the early recognition of heart attack symptoms. Supervised learning models such as KNN and SVM were used in previous studies without feature selection, with datasets obtained from Kaggle. PCA was applied to reduce data dimensions to smaller variables. With the use of confusion matrix and ROC curve evaluations, the accuracy results of the previous KNN model improved from 83.6% to 90.16%. The SVM model also saw an accuracy increase from 85.7% to 86.88%. The use of PCA feature selection demonstrated an improvement in accuracy in the study. The KNN model, with a higher accuracy rate of 90.16%, is better for classifying individuals as normal or diagnosed with a heart attack.

Keywords


Machine learning; KNN; SVM; Classification; Heart Attack

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References


M. Rizwan, S. Arshad, H. Aijaz, R. A. Khan, dan M. Z. U. Haque, “Heart Attack Prediction using Machine Learning Approach,†dalam 2022 Third International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), IEEE, Nov 2022, hlm. 1–8. doi: 10.1109/INTELLECT55495.2022.9969395.

Rokom, “Cegah Penyakit Jantung dengan Menerapkan Perilaku CERDIK dan PATUH.†Diakses: 22 Juni 2024. [Daring]. Tersedia pada: https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20230925/4943963/cegah-penyakit-jantung-dengan-menerapkan-perilaku-cerdik-dan-patuh/#:~:text=Kematian%20di%20Indonesia%20akibat%20penyakit,Matrics%20and%20Evaluation%2C%202019).

J. N, D. P, M. E, R. Santhosh, R. Reshma, dan D. Selvapandian, “Heart Attack Prediction using Machine Learning,†dalam 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, Sep 2022, hlm. 854–860. doi: 10.1109/ICIRCA54612.2022.9985736.

A. Jain, A. Chandra Sekhara Rao, P. Kumar Jain, dan Y.-C. Hu, “Optimized levy flight model for heart disease prediction using CNN framework in big data application,†Expert Syst Appl, vol. 223, hlm. 119859, Agu 2023, doi: 10.1016/j.eswa.2023.119859.

D. Ismafillah, T. Rohana, dan Y. Cahyana, “Implementasi Model Support Vector Machine dan Logistic Regression Untuk Memprediksi Penyakit Stroke,†Jurnal Riset Komputer), vol. 10, no. 1, hlm. 2407–389, 2023, doi: 10.30865/jurikom.v10i1.5478.

K. Tn, S. C. P, M. S, A. Kodipalli, T. Rao, dan S. Kamal, “Prediction of Early Heart Attack Possibility Using Machine Learning,†dalam 2023 2nd International Conference for Innovation in Technology (INOCON), IEEE, Mar 2023, hlm. 1–5. doi: 10.1109/INOCON57975.2023.10100993.

N. Nandal, L. Goel, dan R. TANWAR, “Machine learning-based heart attack prediction: A symptomatic heart attack prediction method and exploratory analysis,†F1000Res, vol. 11, hlm. 1126, Sep 2022, doi: 10.12688/f1000research.123776.1.

C. B. Sonjaya, A. Fitri, N. Masruriyah, D. S. Kusumaningrum, dan A. R. Pratama, “The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease,†INTERNAL (Information System Journal, vol. 5, no. 2, hlm. 166–175, 2022, doi: 10.32627.

M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, dan M. A. Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison,†Comput Biol Med, vol. 136, hlm. 104672, Sep 2021, doi: 10.1016/j.compbiomed.2021.104672.

D. A. Muhammad, R. Amril, dan M. Siregar, “Penerapan Algoritma K-Nearest Neighbord Untuk Prediksi Kematian Akibat Penyakit Gagal Jantung,†vol. III, no. 1, 2022, [Daring]. Tersedia pada: https://www.kaggle.com/andrewmvd/heart-failure-clinical-data.

H. Hasanova, M. Tufail, U.-J. Baek, J.-T. Park, dan M.-S. Kim, “A novel blockchain-enabled heart disease prediction mechanism using machine learning,†Computers and Electrical Engineering, vol. 101, hlm. 108086, Jul 2022, doi: 10.1016/j.compeleceng.2022.108086.

A. K. Gárate-Escamila, A. Hajjam El Hassani, dan E. Andrès, “Classification models for heart disease prediction using feature selection and PCA,†Inform Med Unlocked, vol. 19, hlm. 100330, 2020, doi: 10.1016/j.imu.2020.100330.

A. F. N. Masruriyah, H. Y. Novita, C. E. Sukmawati, S. N. N. Arif, dan A. R. Ramadhan, “Evaluasi Algoritma Pembelajaran Terbimbing terhadap Dataset Penyakit Jantung yang telah Dilakukan Oversampling,†MIND (Multimedia Artificial Intelligent Networking Database) Journal, vol. 8, no. 2, hlm. 242–253, Des 2023.

A. A. Shanbhag, C. Shetty, A. Ananth, A. S. Shetty, K. Kavanashree Nayak, dan B. R. Rakshitha, “Heart Attack Probability Analysis Using Machine Learning,†dalam 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), IEEE, Nov 2021, hlm. 301–306. doi: 10.1109/DISCOVER52564.2021.9663631.

M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, dan A. Raad, “Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability,†Procedia Comput Sci, vol. 203, hlm. 231–238, 2022, doi: 10.1016/j.procs.2022.07.030.

S. P. Barfungpa, H. K. Deva Sarma, dan L. Samantaray, “An intelligent heart disease prediction system using hybrid deep dense Aquila network,†Biomed Signal Process Control, vol. 84, hlm. 104742, Jul 2023, doi: 10.1016/j.bspc.2023.104742.

F. Ali dkk., “A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion,†Information Fusion, vol. 63, hlm. 208–222, Nov 2020, doi: 10.1016/j.inffus.2020.06.008.

Takio Kurita, “Principal Component Analysis (PCA).†Diakses: 20 Juni 2024. [Daring]. Tersedia pada: https://link.springer.com/referenceworkentry/10.1007/978-3-030-03243-2_649-1, 2020.

R. R. Sanni dan H. S. Guruprasad, “Analysis of performance metrics of heart failured patients using Python and machine learning algorithms,†Global Transitions Proceedings, vol. 2, no. 2, hlm. 233–237, Nov 2021, doi: 10.1016/j.gltp.2021.08.028.

M. Wang, X. Yao, dan Y. Chen, “An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients,†IEEE Access, vol. 9, hlm. 25394–25404, 2021, doi: 10.1109/ACCESS.2021.3057693.

S. Faisal, “Implementation of K-Nearest Neighbor Algorithm for Customer Satisfaction,†Buana Information Tchnology and Computer Sciences (BIT and CS, vol. 1, no. 2, 2020.

UNTUNG JAMARI, “PENJELASAN CARA KERJA ALGORITMA K-NEAREST NEIGHBOR (KNN).†Diakses: 19 Juni 2024. [Daring]. Tersedia pada: http://labdas.si.fti.unand.ac.id/2022/03/20/penjelasan-cara-kerja-algoritma-k-nearest-neighbor-knn/

V. Chang, V. R. Bhavani, A. Q. Xu, dan M. Hossain, “An artificial intelligence model for heart disease detection using machine learning algorithms,†Healthcare Analytics, vol. 2, hlm. 100016, Nov 2022, doi: 10.1016/j.health.2022.100016.

S. P. Patro, G. S. Nayak, dan N. Padhy, “Heart disease prediction by using novel optimization algorithm: A supervised learning prospective,†Inform Med Unlocked, vol. 26, hlm. 100696, 2021, doi: 10.1016/j.imu.2021.100696.

E. P. P. Kendrew Huang, “Support Vector Machine Algorithm.†Diakses: 19 Juni 2024. [Daring]. Tersedia pada: https://sis.binus.ac.id/2022/02/14/support-vector-machine-algorithm/

E. R. Lidinillah, T. Rohana, dan A. R. Juwita, “Analisis sentimen twitter terhadap steam menggunakan algoritma logistic regression dan support vector machine,†TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, hlm. 154–164, Jul 2023, doi: 10.37373/tekno.v10i2.440.




DOI: https://doi.org/10.30865/mib.v8i3.7844

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