A Comparative Analysis on the Evaluation of KNN and SVM Algorithms in the Classification of Diabetes
(1) Department of Computer Science, Potensi Utama University
(2) Department of Computer Science, Potensi Utama University
(3) Department of Computer Science, Potensi Utama University
(4) Department of Computer Science, Potensi Utama University
Abstract
Purpose: Diabetes has received a great deal of attention in medical research because of its profound effect on human health. Many factors cause this disease in the human body. Can be from food or drink that is often consumed by the human body. Diabetes cannot be cured and can only be controlled.
Methods: In this study, using 2 data mining techniques namely Support Vector Machine and K-Nearest Neighbor were applied to predict diabetes. In this study, 768 diabetes data were used as trial data, consisting of training data that had been pre-processed data and 400 data cleaning data, 278 data testing data, and 50 diabetes data samples used as samples in the calculation.
Result: The performance of each algorithm is analyzed differently, the results of each best algorithm will be analyzed to determine which algorithm can provide better results for predicting diabetes. The results obtained in this study get a value of 0 where the predicted value of the target class for new data is the negative class (Suffer).
Novelty: This study compares the SVM and K-NN methods for diabetes classification. So, successfully implemented for data on the classification target
Keywords
Full Text:
PDFReferences
P. P. Siswazah, “Universiti Malaysia Perlis Pusat Pengajian Siswazah Centre for Graduate Studies,” pp. 1–3.
G. A. Naikoo and M. U. D. Sheikh, “Development of Highly Efficient NiO Based Composite Materials for Ultra-sensitive Glucose Sensors Non Enzymatic Glucose Sensors,” in Proc. Int. Conf. Sens. Technol., ICST, 2019, pp. 1–4, doi: 10.1109/ICST46873.2019.9047722.
R. Patil and S. Tamane, “A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes,” Int. J. Electr. Comput. Eng., vol. 8, no. 5, pp. 3966–3975, 2018, doi: 10.11591/ijece.v8i5.pp3966-3975.
L. Qiao, Y. Zhu, and H. Zhou, “Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms,” IEEE Access, vol. 8, pp. 104292–104302, 2020, doi: 10.1109/ACCESS.2020.2993937.
K. Puttananjegowda, A. Takshi, and S. Thomas, “Silicon Carbide Nanoparticles Electrospun Nanofibrous Enzymatic Glucose Sensor,” Biosens. Bioelectron., vol. 186, 2021, doi: 10.1016/j.bios.2021.113285.
P. M. Arabi, G. Joshi, S. Lohith, M. Mubashir, P. Y. Pallavi, and M. Surabhi, “Early Diagnosis of Diabetic Vasculopathy by Thermo-Regulatory Vascular Impairment,” in 2020 5th Int. Conf. Commun. Electron. Syst. (ICCES), 2020, pp. 1368–1373, doi: 10.1109/icces48766.2020.9137907.
S. A. D. Alalwan, “Diabetic Analytics: Proposed Conceptual Data Mining Approaches in Type 2 Diabetes Dataset,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 1, pp. 85–95, 2019, doi: 10.11591/ijeecs.v14.i1.pp88-95.
S. Wong, “Stock Price Prediction Model Based on the Short-term Trending of KNN Method,” in 2020 7th Int. Conf. Inf. Sci. Control Eng., ICISCE 2020, 2020, pp. 1355–1360, doi: 10.1109/ICISCE50968.2020.00273.
L. R. Somula and M. Meena, “K-Nearest Neighbour (KNN) Algorithm based Cooperative Spectrum Sensing in Cognitive Radio Networks,” in Proc. 4th Int. Conf. Cybern. Cogn. Mach. Learn. Appl., ICCCMLA 2022, 2022, pp. 1–6, doi: 10.1109/ICCCMLA56841.2022.9988996.
R. Zhang, T. Du, S. Qu, and H. Sun, “Adaptive Density-based Clustering Algorithm with Shared KNN Conflict Game,” Inf. Sci. (Ny)., vol. 565, pp. 344–369, 2021, doi: 10.1016/j.ins.2021.02.017.
S. Di̇Lmaç, Z. Dokur, and T. Ölmez, “Comparative Analysis of MABC with KNN, SOM, and ACO Algorithms for ECG Heartbeat Classification,” Turkish J. Electr. Eng. Comput. Sci., vol. 26, no. 6, pp. 2819–2830, 2018, doi: 10.3906/elk-1712-328.
A. Purwanto and L. Parningotan Manik, “Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines,” Sci. J. Inform., vol. 10, no. 1, pp. 1–12, 2023, doi: 10.15294/sji.v10i1.39865.
U. I. Larasati, M. A. Muslim, R. Arifudin, and A. Alamsyah, “Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis,” Sci. J. Inform., vol. 6, no. 1, pp. 138–149, 2019, doi: 10.15294/sji.v6i1.14244.
N. A. Noori and A. A. Yassin, “A Comparative Analysis for Diabetic Prediction Based on Machine Learning Techniques,” J. Basrah Res., vol. 47, no. 1, pp. 180–190, 2021, [Online]. Available: https://www.iasj.net/iasj/journal/260/issues.
L. Mohan, J. Pant, P. Suyal, and A. Kumar, “Support Vector Machine Accuracy Improvement with Classification,” in 2020 12th Int. Conf. Comput. Intell. Commun. Netw., CICN 2020, 2020, pp. 477–481, doi: 10.1109/CICN49253.2020.9242572.
S. E. Pandarakone, S. Gunasekaran, Y. Mizuno, and H. Nakamura, “Application of Naive Bayes Classifier Theorem in Detecting Induction Motor Bearing Failure,” in 2018 23rd Int. Conf. Electr. Mach., ICEM 2018, 2018, pp. 1761–1767, doi: 10.1109/ICELMACH.2018.8506836.
N. G. Ramadhan, “Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus,” Sci. J. Inform., vol. 8, no. 2, pp. 276–282, 2021, doi: 10.15294/sji.v8i2.32484.
C. Chethana, “Prediction of Heart Disease Using Different KNN Classifier,” in 5th Int. Conf. Intell. Comput. Control Syst., ICICCS 2021, 2021, pp. 1186–1194, doi: 10.1109/ICICCS51141.2021.9432178.
J. Huang, Y. Wei, J. Yi, and M. Liu, “An Improved KNN based on Class Contribution and Feature Weighting,” in 10th Int. Conf. Meas. Technol. Mechatron. Autom., ICMTMA 2018, 2018, pp. 313-316, doi: 10.1109/ICMTMA.2018.00083.
Q. Yunneng, “A New Stock Price Prediction Model based on Improved KNN,” in 2020 7th Int. Conf. Inf. Sci. Control Eng., ICISCE 2020, 2020, pp. 77–80, doi: 10.1109/ICISCE50968.2020.00026.
S. A. W. Fitra A. Bachtiar, Indra K. Syahputra, “Perbandingan Algoritme Machine Learning Untuk Memprediksi,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 5, pp. 543–548, 2019, doi: 10.25126/jtiik.2019611755.
Y. Yustikasari, H. Mubarok, and R. Rianto, “Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients,” Sci. J. Inform., vol. 9, no. 2, pp. 205–217, 2022, doi: 10.15294/sji.v9i2.32369.
A. Satria, O. S. Sitompul, and H. Mawengkang, “5-Fold Cross Validation on Supporting K-Nearest Neighbour Accuration of Making Consimilar Symptoms Disease Classification,” in 2nd Int. Conf. Comput. Sci. Eng.: Eff. Digit. World Pandemic (EDWAP), IC2SE 2021, 2021, pp. 1–5, doi: 10.1109/IC2SE52832.2021.9792094.
C. J. Ma and Z. sheng Ding, “Improvement of K-Nearest Neighbor Algorithm based on Double Filtering,” in 2020 5th Int. Conf. Mech. Control Comput. Eng., ICMCCE 2020, 2020, pp. 1567–1570, doi: 10.1109/ICMCCE51767.2020.00343.
M. A. Alanezi and A. Alqaddoumi, “Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm,” in 2020 Int. Conf. Data Anal. Bus. Ind.: Way Towards Sustain. Econ., ICDABI 2020, 2020, pp. 1–6, doi: 10.1109/ICDABI51230.2020.9325700.
Novita, Suyanto, and Y. Prasti Eko, “Bonferroni Mean Fuzzy K-Nearest Neighbors Based Handwritten Chinese Character Recognition,” in 2021 Int. Conf. Data Sci. Appl., ICoDSA 2021, 2021, pp. 118–123, doi: 10.1109/ICoDSA53588.2021.9617488.
R. Zhao, “The Water Potability Prediction Based on Active Support Vector Machine and Artificial Neural Network,” in 2021 Int. Conf. Big Data Artif. Intell. Risk Manag., ICBAR 2021, 2021, pp. 110–114, doi: 10.1109/ICBAR55169.2021.00032.
D. A. Anggoro and D. Novitaningrum, “Comparison of Accuracy Level of Support Vector Machine (SVM) and Artificial Neural Network (ANN) Algorithms in Predicting Diabetes Mellitus Disease,” ICIC Express Lett., vol. 15, no. 1, pp. 9–18, 2021, doi: 10.24507/icicel.15.01.9.
P. C. Upadhyay, L. Karanam, J. A. Lory, and G. N. Desouza, “Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble,” in 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, 2021, pp. 1–7, doi: 10.1109/SSCI50451.2021.9660147.
X. Huang and S. Wang, “Prediction of Bottom-hole Flow Pressure in Coalbed Gas Wells based on GA Optimization SVM,” in Proc. 2018 IEEE 3rd Adv. Inf. Technol. Electron. Autom. Control Conf., IAEAC 2018, 2018, pp. 138–141, doi: 10.1109/IAEAC.2018.8577488.
V. Ariyani, P. Putri, A. B. Prasetijo, and D. Eridani, “Perbandingan Kinerja Algoritme Naïve Bayes Dan K-Nearest Neighbor (Knn) Untuk Prediksi Harga Rumah,” Transm. J. Ilm. Tek. Elektro, vol. 4, no. 24, pp. 162–171, 2022, doi: https://doi.org/10.14710/transmisi.24.4.162-171.
N. H. Othman, K. Y. Lee, A. R. M. Radzol, W. Mansor, and U. R. M. Rashid, “Classification of Salivary Adulterated NS1 SERS Spectra Using PCA-Cosine-KNN,” in ICIIBMS 2019 - 4th Int. Conf. Intell. Inform. Biomed. Sci., 2019, pp. 159–163, doi: 10.1109/ICIIBMS46890.2019.8991490.
R. Ramadhan and A. Triyudi, “Perbandingan Klasifikasi Data Calon Pendonor Darah Dengan Algoritma K-Nearest Neighbor Dan Support Vector Machine,” J. Tek. Inform. dan Sist. Inf., vol. 9, no. 2, pp. 1250–1260, 2022, doi: 10.35957/jatisi.v9i2.2018.
M. I. Perangin-angin, A. H. Lubis, and A. Ikhwan, “Association Rules Analysis on FP-Growth Method in Predicting Sales,” Int. J. Recent Trends Eng. Res., vol. 3, no. 10, pp. 58–65, 2017, doi: 10.23883/ijrter.2017.3453.dhcoa.
S. Wang et al., “K-Means Clustering With Incomplete Data,” IEEE Access, vol. 7, pp. 69162–69171, 2019, doi: 10.1109/ACCESS.2019.2910287.
D. Hartama, A. Perdana Windarto, and A. Wanto, “The Application of Data Mining in Determining Patterns of Interest of High School Graduates,” J. Phys. Conf. Ser., vol. 1339, no. 1, p. 012042, Dec. 2019, doi: 10.1088/1742-6596/1339/1/012042.
T. Yue, Y. Li, and Z. Hu, “Dwsa: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining,” Electron., vol. 10, no. 19, pp. 1–16, 2021, doi: 10.3390/electronics10192443.
A. Ibrahim, “Forecasting the Early Market Movement in Bitcoin Using Twitter’s Sentiment Analysis: An Ensemble-based Prediction Model,” in 2021 IEEE Int. IOT Electron. Mechatron. Conf. IEMTRONICS 2021 - Proc., 2021, pp. 1–5, doi: 10.1109/IEMTRONICS52119.2021.9422647.
A. Kumar, A. Verma, G. Shinde, Y. Sukhdeve, and N. Lal, “Crime Prediction Using K-Nearest Neighboring Algorithm,” in Int. Conf. Emerg. Trends Inf. Technol. Eng., ic-ETITE 2020, 2020, pp. 1–4, doi: 10.1109/ic-ETITE47903.2020.155.
Y. Jusman et al., “Comparison between Support Vector Machine and K-Nearest Neighbor Algorithms for Leukemia Images Classification Using Shape Features,” in ICSEC 2021 - 25th Int. Comput. Sci. Eng. Conf., 2021, pp. 70–74, doi: 10.1109/ICSEC53205.2021.9684585.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.