EXPLORATORY DATA ANALYSIS OF CLINICAL HEART FAILURE USING A SUPPORT VECTOR MACHINE
Main Article Content
Abstract
This study aims to explore the clinical data of patients diagnosed with heart failure using the Support Vector Machine (SVM) algorithm as a classification method. Clinical data from verified patients has been collected and analyzed to identify patterns, associations, and risk factors contributing to heart failure risk. The exploratory data analysis results reveal essential clinical data characteristics and provide initial insight into patient profiles and clinical variables that can influence heart failure risk. The SVM model was built to predict the risk of heart failure based on clinical data. This model is evaluated using classification metrics such as F1-Score and accuracy. Evaluation results show good performance with an F1-Score reaching 0.83, which indicates a reasonable degree of accuracy and balance in predicting the risk of heart failure. The conclusion of this study shows the potential of the classification model as a tool in managing heart failure patients. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data.
Keywords: Exploratory Data Analysis, Heart Failure, Classification, Python, Support Vector Machine
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:
- Copyright to the manuscripts of scientific papers in this Journal is held by the author.
- The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
- Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
- Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.
References
Fredilio, J. Rahmad, S. Hamonangan Sinurat,
D. Ryan Hamonangan Sitompul, D. Jusuf Ziegel, and E. Indra, “Perbandingan Algoritma K-Nearest Neighbors (K-NN) dan Random forest terhadap Penyakit Gagal Jantung,” Jurnal Teknlogi Informatika dan Komputer MH. Thamrin, vol. 9, no. 1, pp. 471–486, Mar. 2023, doi: 10.37012/jtik.v9i1.1432.
A. P. Lumi, V. F. F. Joseph, and N. C. I. Polii, “Rehabilitasi Jantung pada Pasien Gagal Jantung Kronik,” Jurnal Biomedik:JBM, vol. 13, no. 3, p. 309, Apr. 2021, doi: 10.35790/jbm.v13i3.33448.
R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” in Procedia Computer
Science, 2020. doi: 10.1016/j.procs.2020.06.017.
S. Aldera, A. Emam, M. Al-Qurishi, M. Alrubaian, and A. Alothaim, “Exploratory Data Analysis and Classification of a New Arabic Online Extremism Dataset,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3132651.
K. Sahoo*, A. K. Samal, J. Pramanik, and S. K. Pani, “Exploratory Data Analysis using Python,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 4727–4735, Oct. 2019, doi: 10.35940/ijitee.L3591.1081219.
A. Kulkarni and A. Shivananda, Natural Language Processing Recipes. Berkeley, CA: Apress, 2019. doi: 10.1007/978-1-4842-4267- 4.
A. Arifin, J. Hendyli, and D. E. Herwindiati, “Klasifikasi Tanaman Obat Herbal Menggunakan Metode Support Vector Machine,” Computatio : Journal of Computer Science and Information Systems, vol. 5, no. 1, 2021, doi: 10.24912/computatio.v1i1.12811.
Q. Hasanah, H. Oktavianto, and Y. D. Rahayu, “Analisis Algoritma Gaussian Naive Bayes Terhadap Klasifikasi Data Pasien Penderita Gagal Jantung,” Jurnal Smart Teknologi, vol. 3, no. 4, pp. 382–389, 2022.
S. Saida, H. Haryati, and L. Rangki, “Kualitas Hidup Penderita Gagal Jantung Kongestif Berdasarkan Derajat Kemampuan Fisik dan Durasi Penyakit,” Faletehan Health Journal, vol. 7, no. 02, pp. 70–76, Jul. 2020, doi: 10.33746/fhj.v7i02.134.
I. P. Putri, “Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular,” Indonesian Journal of Data and Science, vol. 2, no. 1, pp. 21–28, Mar. 2021, doi: 10.33096/ijodas.v2i1.25.
H. Christanto, J. Rahmad, S. Hamonangan Sinurat, D. Ryan Hamonangan Sitompul, A. Sitomorang, and D. Jusuf Ziegel, “Analisis Perbandingan Decision Tree, Support Vector Machine, dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor,” Jurnal Teknlogi Informatika dan Komputer MH. Thamrin, vol. 9, no. 1, pp. 306–319, 2023, doi: 10.37012/jtik.v9i1.1429.
R. Kotha, “Heart Failure Clinical Records Classification,” Kaggle, 2021.
D. T. Husni et al., “ANALISIS BIG DATA PENJUALAN VIDEO GAMES MENGUNAKAN EDA,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 1, p. 43, Jun. 2022, doi: 10.37600/tekinkom.v5i1.517.
D. Y. Siringoringo, V. Sihombing, and M. Masrizal, “SISTEM INFORMASI PENJUALAN DAN PERSEDIAAN
PRODUK PERALATAN PERTANIAN
BERBASIS WEB,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 4, no. 1, pp. 54–59, 2021, doi:
37600/tekinkom.v4i1.232.
M. Radhi, D. Ryan Hamonangan Sitompul, S. Hamonangan Sinurat, and E. Indra, “PREDIKSI HARGA MOBIL MENGGUNAKAN ALGORITMA REGRESSI DENGAN HYPER- PARAMETER TUNING,” Jurnal Sistem Informasi dan Ilmu Komputer Prima, vol. 4, no. 2, 2021.
M. M. Hassan, M. Z. Uddin, A. Mohamed, and
A. Almogren, “ A robust human activity recognition system using smartphone sensors and Deep Learning,” Future Generation Computer Systems, vol. 81, pp. 307–313, 2018.
A. Kumar, “Learning predictive analytics with python: Gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with pyton,” Packt Publishing, 2016.
R. Resmiati and T. Arifin, “Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination,” SISTEMASI, vol. 10, no. 2, 2021, doi: 10.32520/stmsi.v10i2.1238.
Oryza Habibie Rahman, Gunawan Abdillah, and Agus Komarudin, “Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, 2021, doi: 10.29207/resti.v5i1.2700.
A. Y and K. Y. S, “Multiclass Classification and Support Vector Machine,” Global Journal of Computer Science and Technology Interdisciplinary, vol. 12, no. 11, pp. 14–19, 2012.
R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document- level sentiment classification: An empirical comparison between SVM and ANN,” Expert Syst Appl, vol. 40., no. 2, pp. 621–633, 2013.