Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree
DOI:
https://doi.org/10.57152/predatecs.v2i1.1103Keywords:
Classification Algorithms, Decision Tree, Diabetes Mellitus, K-Nearest Neighbor, Naïve BayesAbstract
The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification
References
K. M. Varma and D. B. s Panda, “Comparative analysis of Predicting Diabetes Using Machine Learning Techniques,” JETIR, vol. 6, no. 6, pp. 522–530, 2019.
A. Nila Yuliawati, P. Made Desy Ratnasari, and P. Riski Rosalina, “Knowledge and Quality of Life in Type 2 Diabetes Mellitus Patients also its Related Factors,” JMPF, vol. 12, no. 1, pp. 14–27.
H.Muthukrishnan, T.Abarna, K.L.Gherija, and K.L.Gherija, “Comparison of Machine Learning Algorithms in Predicting Diabetes Mellitus,” vol. 25, no. 5, pp. 621–634, 2021.
I. H. Sarker, F. Faruque, H. Alqahtani, and A. Kalim, “K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services,” EAI Endorsed Transactions on Scalable Information Systems, vol. 7, no. 26, pp. 1–9, 2020, doi: 10.4108/eai.13-7-2018.162737.
Md. F. Faruque, Asaduzzaman, and I. H. Sarker, “Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus,” IEEE.
M. Tombokan et al., “HUBUNGAN DUKUNGAN KELUARGA DENGAN MOTIVASI DALAM MENGONTROL KADAR GULA DARAH PADA PENDERITA DIABETES MELITUS DI WILAYAH KERJA PUSKESMAS PAMPANG KECAMATAN PANAKKUKANG KOTA MAKASSAR Family Support Relationship With Motivation In Control Drinking Blood Sugar To Patients Diabetes Melitus At Pampang Public Health Center Panakkukang of Makassar City,” 2017.
N. Sakagraha Kuspinta, A. W. Widodo, and M. T. Furqon, “Penentuan Menu Makanan Untuk Penderita Diabetes Menggunakan Metode Iterative Dichotomizer Tree (ID3),” 2018. [Online]. Available: http://j-ptiik.ub.ac.id
R. Patel, “Diabetes Prediction using Data Mining Techniques - A Comparative Study,” JETIR, vol. 10, no. 6, pp. 818–824, 2023.
N. Azizah, M. R. Firdaus, R. Suyaningsih, F. Indrayatna, and U. Padjadjaran, “Penerapan Algoritma Klasifikasi K-Nearest Neighbor pada Penyakit Diabetes,” 2023, [Online]. Available: http://prosiding.snsa.statistics.unpad.ac.id
F. Sholekhah, A. D. Putri, and L. Efrizoni, “Comparison of Naive Bayes and K-Nearest Neighbors Algorithms for Metabolic Syndrome Classification Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbors untuk Klasifikasi Metabolik Sindrom,” vol. 4, no. April, pp. 507–514, 2024.
L. U. Khasanah, Y. N. Nasution, F. Deny, and T. Amijaya, “Klasifikasi Penyakit Diabetes Melitus Menggunakan Algoritma Naïve Bayes Classifier,” vol. 1, no. 1, pp. 41–50, 2022, [Online]. Available: http://jurnal.fmipa.unmul.ac.id/index.php/basis
B. A. Maulana and M. J. Fahmi, “Sentiment Analysis of Pluang Applications With Naive Bayes and Support Vector Machine ( SVM ) Algorithm Analisis Sentimen Terhadap Aplikasi Pluang Menggunakan Algoritma Naive Bayes dan Support Vector Machine ( SVM ),” vol. 4, no. April, pp. 375–384, 2024.
M. Alehegn, R. R. Joshi, and P. Mulay, “Diabetes Analysis And Prediction Using Random Forest , K-NN , Naïve Bayes , And J48?: An Ensemble Approach,” vol. 8, no. 09, 2019.
M. E. Febrian, K. M. Suryanigrum, and R. Yunanda, “ScienceDirect ScienceDirect 7th International Conference on Computer Science and Computational Intelligence 2022 Diabetes prediction using supervised machine learning Diabetes prediction using supervised machine learning,” Procedia Computer Science, vol. 216, no. 2022, pp. 21–30, 2023, doi: 10.1016/j.procs.2022.12.107.
A. Purnamawati, W. Nugroho, D. Putri, and W. F. Hidayat, “Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree , Random Forest , Naïve Bayes , SVM dan K-NN,” InfoTekJar?: Jurnal Nasional Informatika dan Deteksi, vol. 1, no. 1, pp. 212–215, 2020.
E. Novianto, A. Hermawan, and D. Avianto, “KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1,” Rabit?: Jurnal Teknologi dan Sistem Informasi Univrab, vol. 8, no. 2, pp. 146–154, Jul. 2023, doi: 10.36341/rabit.v8i2.3434.
D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 1578–1585. doi: 10.1016/j.procs.2018.05.122.
J. P. Kandhasamy and S. Balamurali, “Performance analysis of classifier models to predict diabetes mellitus,” in Procedia Computer Science, Elsevier B.V., 2015, pp. 45–51. doi: 10.1016/j.procs.2015.03.182.
A. Zaharim and WSEAS (Organization), Applied computational science?: proceedings of the 13th international conference on applied computer and applied computational science (ACACOS ’14)?: Kuala Lumpur, Malaysia, April 23-25 2014.
A. M. Argina, “Indonesian Journal of Data and Science Penerapan Metode Klasifikasi K-Nearest Neigbor pada Dataset Penderita Penyakit Diabetes,” vol. 1, no. 2, pp. 29–33, 2020.
A. H. Khaleel, G. A. Al-Suhail, and B. M. Hussan, “International Journal of Computer Science and Mobile Computing A Weighted Voting of K-Nearest Neighbor Algorithm for Diabetes Mellitus,” 2017. [Online]. Available: www.ijcsmc.com
“Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM Sumarlin STIKOM Uyelindo Kupang.”
“293-Article Text-1245-1-10-20190421”.
D. Susilawati and D. Riana, “Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus,” 2019.
A. Sachdeva, P. Kumar, O. P. Yadav, R. K. Garg, and A. Gupta Editors, “Operations Management and Systems Engineering.” [Online]. Available: http://www.springer.com/series/15734
