Optimization of Genetic Algorithm from Comparison of Machine Learning for Heart Disease Prediction
DOI:
https://doi.org/10.26418/justin.v13i2.87898Keywords:
Decision Tree, Random Forest, Gradien Boosted Tree, Genetic Algorithm OptimizationAbstract
Ischemic Heart Disease (IHD) is the leading cause of death worldwide, accounting for 13% of global fatalities. The number of deaths caused by IHD rose from 2.7 million in 2000 to 9.1 million in 2021, an increase of 6.4 million. IHD can be diagnosed through medical examinations or various health tests, as well as by leveraging technological advancements in artificial intelligence to enable early disease detection. This early detection is crucial for preventing heart disease, as there is currently no cure for the condition. This study aims to compare machine learning algorithms based on decision tree methods (Decision Tree, Random Forest, and Gradient Boosted Tree) with optimization using genetic algorithms to predict heart disease. The dataset used includes information from 8,625 patients who have experienced heart attacks, featuring attributes such as Sex, General Health, Age Category, Height (in meters), Weight (in kilograms), BMI, and "Had Heart Attack" as the label attribute. The initial modeling phase involved splitting the data into 80% for training (6,900 samples) and 20% for testing (1,725 samples). The results showed that the Random Forest model achieved the highest accuracy at 95.26%, narrowly surpassing the Decision Tree model, which attained 95.22%, by 0.04%. Meanwhile, the Gradient Boosted Tree model demonstrated the lowest accuracy at 90.99%. Subsequently, the application of the Genetic Algorithm significantly improved the accuracy, precision, and recall metrics across all three models, although the recall value for the Gradient Boosted Tree model decreased by 5.17%.References
World Health Organization, “The Top 10 Causes of Death,†Health Topics. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
M. A. Sembiring, “Analisis Faktor Prediksi Diagnosa Tingkat Serangan Jantung Menggunakan Metode Regression,†J. Tek., vol. 4, no. 1, pp. 16–22, 2024, doi: https://doi.org/10.54314/teknisi.v4i1.1800.
M. C. Arta, N. Anwar, Y. A. Putri, S. Suharjito, and M. Asroll, “Implementasi Prediksi Penyakit Jantung Menggunakan Data Mining Untuk Dunia Kesehatan,†J. Optim., vol. 10, no. 1, pp. 42–48, 2024, doi: https://doi.org/10.35308/jopt.v10i1.9075.
M. F. Aditya, A. Pramuntadi, D. P. Wijaya, and Y. Wicaksono, “Implementasi Metode Decision Tree pada Prediksi Penyakit Diabetes Melitus Tipe 2,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 3, pp. 1104–1110, 2024, doi: https://doi.org/10.57152/malcom.v4i3.1284.
M. H. Fikri, M. M. Ulum, and N. L. D. M. Sari, “Literature review Implementasi kecerdasan buatan untuk Prediksi dan Pengelolaan Kemacetan Lalu Lintas di Perkotaan,†in Informatika, IN-FEST (Informatics Festival) Seminar Nasional, 2024, pp. 449–456. [Online]. Available: https://conference.upgris.ac.id/index.php/infest/article/view/5772/4492
R. E. Nugroho, W. Y. Pamungkas, and J. H. Jaman, “Pendeteksi Penyakit Hepatitis Menggunakan CART Decision Tree,†JITET (Jurnal Inform. dan Tek. Elektro Ter., vol. 12, no. 3S1, pp. 3690–3696, 2024, doi: http://dx.doi.org/10.23960/jitet.v12i3S1.5184.
C. N. Syahputri and M. S. Hasibuan, “Optimasi Klasifikasi Decision Tree Dengan Teknik Pruning Untuk Mengurangi Overfitting,†J. Sist. Inf., vol. 11, no. 2, pp. 87–96, 2024, doi: https://doi.org/10.30656/jsii.v11i2.9161.
T. R. Nangon and A. D. Alexander, “Prediksi Tahap Awal Penyakit Jantung Menggunakan Algoritma Random Forest (Studi Kasus RSIJ),†Dawatuna J. Commun. Islam. Broadcast., vol. 4, no. 4, pp. 1561–1567, 2024, doi: 10.47467/dawatuna.v4i4.1882.
F. Firmansyah and A. Yulianto, “Pemodelan Pembelajaran Mesin untuk Prediksi Kesehatan Mental di Tempat Kerja,†J. dan Penelit. Manaj. Inform., vol. 13, no. 1, pp. 397–407, 2024, doi: 10.33395/jmp.v13i1.13674.
H. D. Siswaja and Y. Ramdhani, “Pendekatan Algoritma Neural Network Dan Genetic Algorithm Untuk Prediksi Penyakit Ginjal Kronis,†J. RESPONSIF, vol. 6, no. 2, pp. 232–239, 2024, doi: https://doi.org/10.51977/jti.v6i2.1778.
A. Patue, G. F. Sidik, A. Affandy, and A. R. Ismail, “Sentimen Analisis Komentar Masyarakat terhadap Pelayanan Perizinan Berusaha menggunakan Algoritma Naive Bayes dengan Seleksi Genetika Algoritma,†Sist. J. Sist. Inf., vol. 13, no. 1, pp. 252–259, 2024, doi: https://doi.org/10.32520/stmsi.v13i1.3550.
R. P. Nugraha and B. Soewito, “Deteksi Akurasi False Positive Pada Sistem Deteksi Intrusi Menggunakan K-NN (K Nearest Neighbor),†COSTING J. Econ. Bus. Account., vol. 7, no. 5, pp. 4321–4329, 2024, doi: https://doi.org/10.31539/costing.v7i5.12297.
V. N. Praniasty, Z. F. Hunusalela, and S. Sinambela, “Penjadwalan Produksi Untuk Meminimumkan Nilai Makespan Dengan Metode Algoritma Genetika Dan Algoritma Tabu Search Pada PT Karsa Wijaya Pratama,†J. Ind. Eng. Technol., vol. 4, no. 2, pp. 102–109, 2024, doi: https://doi.org/10.24176/jointech.v4i2.12496.
M. A. Aziz, R. Santoso, and B. Warsito, “Prediksi Calon Nasabah Gadai Potensial Pada Pt. Pegadaian (Persero) Menggunakan Support Vector Machine Dengan Algoritma Genetika,†J. GAUSSIAN, vol. 13, no. 2, pp. 300–307, 2024, doi: https://doi.org/10.14710/j.gauss.13.2.300-307.
P. K. Bhowmik et al., “Advancing Heart Disease Prediction through Machine Learning: Techniques and Insights for Improved Cardiovascular Health,†Br. J. Nurs. Stud., pp. 35–49, 2024, doi: 10.32996/bjns.2024.4.2.5.
P. K. Pande, P. Khobragade, S. N. Ajani, and V. P. Uplanchiwar, “Early Detection and Prediction of Heart Disease with Machine Learning Techniques,†in 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), 2024. doi: 10.1109/ICICET59348.2024.10616294.
S. Saha, M. M. Rahman, T. T. Suki, M. M. Alam, M. S. Alam, and M. A. S. Haque, “Heart Disease Prediction Using Machine Learning Algorithms: Performance Analysis,†in 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 2024. doi: 10.1109/ICAEEE62219.2024.10561820.
G. James, D. Witten, T. Hastie, and R. Tibshirani, Introduction to Statistical Learning, Second Edi. Springer Nature, 2021. doi: , https://doi.org/10.1007/978-1-0716-1418-1.
R. Pramudita, S. Muis, N. Safitri, and F. Shafirawati, “Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara,†J. Teknol ogi Inf. Komuni kasi (e- J. ), vol. 11, no. 1, pp. 128–134, 2024, doi: https://doi.org/10.38204/tematik.v11i1.1928.
M. R. Pahlevi, E. Rasywir, Y. Pratama, F. Fachruddin, M. Istoningtyas, and M. Yaasin, “Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder,†Build. Informatics, Technol. Sci., vol. 5, no. 4, pp. 672–681, 2024, doi: 10.47065/bits.v5i4.4864.
E. Fammaldo and M. Lestari, “Gradient Boosting Trees Untuk Pemodelan Dan Prediksi Biaya Kerugian Asuransi Mobil,†J. Algoritm. Log. dan Komputasi, vol. 7, no. 1, pp. 634–642, 2024, doi: http://dx.doi.org/10.30813/j-alu.v2i2.6030.
M. C. Yustina, I. N. Ichsan, and G. M. Suranegara, “Implementasi Algoritma Genetika Proses Mutasi Differential Evolution Pada Sistem Penjadwalan Mata Pelajaran,†KLIK Kaji. Ilm. Inform. dan Komput., vol. 5, no. 1, pp. 116–130, 2024, doi: 10.30865/klik.v5i1.2109.
F. Novianti and N. Ulinnuha, “Seleksi Fitur Algoritma Genetika Dalam Klasifikasi Data Rekam Medis PCOS Menggunakan SVM,†J. Ilm. NERO, vol. 9, no. 1, pp. 9–19, 2024, doi: https://doi.org/10.21107/nero.v9i1.25399.
David Edward Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc., 1989. [Online]. Available: http://www2.fiit.stuba.sk/~kvasnicka/Free books/Goldberg_Genetic_Algorithms_in_Search.pdf
Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Second Edi. O’Reilly Media, 2021.
D. Kurniawan, M. Wahyudi, L. Pujiastuti, and S. Sumanto, “Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Forest,†Indones. J. Comput. Sci., vol. 3, no. 1, pp. 51–56, 2024, doi: https://doi.org/10.31294/ijcs.v3i1.6071.
M. Iqbal, S. Miskiyah, S. L. Sham, S. Anwar, and M. H. Fuad, “Perbandingan Metode Decision Tree Dan Naive Bayes Pada Tingkat Penjualan Minuman Kopi Di Kopi Pawon Nusantara,†J. Insa. (Journal Inf. Syst. Manag. Innov., vol. 4, no. 1, pp. 27–34, 2024, doi: https://doi.org/10.31294/jinsan.v4i1.3682.
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