Predicting the Conversion from Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Mestizo Patients Using Gaussian Naive Bayes Classifier: A Prospective Cohort Study

Nurul Kholifah Yulinda

doi:10.56705/ijaimi.v2i1.139

Authors

Nurul Kholifah Yulinda Universitas Muslim Indonesia

DOI:

https://doi.org/10.56705/ijaimi.v2i1.139

Keywords:

Clinically Isolated Syndrome, Multiple Sclerosis, Gaussian Naive Bayes, Predictive Modeling, Prospective Cohort Study

Abstract

This study explores the application of the Gaussian Naive Bayes (GNB) classifier to predict the conversion from Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) among Mexican mestizo patients. Utilizing a dataset gathered from the National Institute of Neurology and Neurosurgery in Mexico City, which included patients diagnosed with CIS between 2006 and 2010, we employed a prospective cohort study design. Our approach involved preprocessing the data to handle missing values and scale normalization followed by splitting it into training and testing subsets. The GNB model's performance was assessed through a 5-fold cross-validation, focusing on accuracy, precision, recall, and F1-score. Results demonstrated the model's capability to predict MS conversion with reasonable precision, highlighted by a significant peak in performance metrics in the third fold of the validation. The study addresses a gap in predictive diagnostics for MS within a specific demographic group, providing valuable insights for early intervention strategies. Despite some limitations such as the model's sensitivity to data heterogeneity and the demographic specificity of the cohort, the findings underscore the potential for predictive models in clinical settings. Recommendations for future research include the use of more sophisticated algorithms and broader demographic studies to enhance predictive accuracy and generalizability.

References

M. V Anand, “Gaussian Naïve Bayes Algorithm: A Reliable Technique Involved in the Assortment of the Segregation in Cancer,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/2436946.

P. Mansourian, “Anomaly Detection for Connected Autonomous Vehicles Using LSTM and Gaussian Naïve Bayes,” Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, vol. 509, pp. 31–43, 2023, doi: 10.1007/978-3-031-34851-8_3.

P. Venkata, “Data mining model and Gaussian Naive Bayes based fault diagnostic analysis of modern power system networks,” Mater. Today Proc., vol. 62, pp. 7156–7161, 2022, doi: 10.1016/j.matpr.2022.03.035.

I. Sulistiani, “Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms,” 2022 1st Int. Conf. Inf. Syst. Inf. Technol. ICISIT 2022, pp. 170–175, 2022, doi: 10.1109/ICISIT54091.2022.9872808.

M. Gayathri, “Analysis of Accuracy in Anomaly Detection of Intrusion Detection System using Naïve Bayes Algorithm compared Over Gaussian model,” ECS Trans., vol. 107, no. 1, pp. 13977–13991, 2022, doi: 10.1149/10701.13977ecst.

A. J. Meerja, “Gaussian naïve bayes based intrusion detection system,” Adv. Intell. Syst. Comput., vol. 1182, pp. 150–156, 2021, doi: 10.1007/978-3-030-49345-5_16.

A. Krysovatyy, “Classification Method of Fictitious Enterprises Based on Gaussian Naive Bayes,” Int. Sci. Tech. Conf. Comput. Sci. Inf. Technol., vol. 2, pp. 224–227, 2021, doi: 10.1109/CSIT52700.2021.9648584.

H. Azis, F. T. Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, 2020, [Online]. Available: file:///Users/kbh/Library/Application Support/Mendeley Desktop/Downloaded/Azis, Admojo, Susanti - 2020 - Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah.pdf.

H. Azis, F. Fattah, and P. Putri, “Performa Klasifikasi K-NN dan Cross-validation pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, [Online]. Available: file:///Users/kbh/Downloads/507-2012-5-PB.pdf.

M. M. Baharuddin, T. Hasanuddin, and H. Azis, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 28, pp. 269–274, 2019, [Online]. Available: file:///Users/kbh/Library/Application Support/Mendeley Desktop/Downloaded/Baharuddin, Hasanuddin, Azis - 2019 - Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca.pdf.

A. Fitria and H. Azis, “Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 102–106, 2018, [Online]. Available: file:///Users/kbh/Library/Application Support/Mendeley Desktop/Downloaded/Fitria, Azis - 2018 - Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier.pdf.

F. T. Admojo and Ahsanawati, “Klasifikasi Aroma Alkohol Menggunakan Metode KNN,” Indones. J. Data Sci., vol. 1, no. 2, pp. 34–38, 2020.

A. Maulida, “Penerapan Metode Klasifikasi K-Nearest Neigbor pada Dataset Penderita Penyakit Diabetes,” Indones. J. Data Sci., vol. 1, no. 2, pp. 29–33, 2020.

F. Tangguh and Y. Islami, “Analisis performa algoritma Stochastic Gradient Descent ( SGD ) dalam mengklasifikasi tahu berformalin,” Indones. J. Data Sci., vol. 3, no. 1, pp. 1–8, 2022, doi: 10.56705/ijodas.v3i1.42.

N. A’ayunnisa, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” … J. Data Sci., 2022, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/54.

Y. I. Sulistya, “Analisis perbandingan Reduction Technique dengan metode Dimentional Reduction dan Cross Validation pada dataset Breast Cancer,” Indones. J. Data Sci., vol. 3, no. 2, pp. 82–88, 2022, doi: 10.56705/ijodas.v3i2.41.

Ericha Apriliyani and Y. Salim, “Analisis performa metode klasifikasi Naïve Bayes Classifier pada Unbalanced Dataset,” Indones. J. Data Sci., vol. 3, no. 2, pp. 47–54, 2022, doi: 10.56705/ijodas.v3i2.45.