Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models
DOI:
https://doi.org/10.51519/journalisi.v5i4.568Keywords:
Classification, Clustering, Pregnancy-risk, Web-basedAbstract
This study aims to determine whether there is an increase in accuracy results for predicting pregnancy risk with a classification algorithm that goes through and without going through the clustering stage. After that, compare which classification algorithm gets the best improvement. This study uses the K-Means clustering approach, as well as the SVM, Naive Bayes, and K-Nearest Neighbor (KNN) classification algorithms. The pregnancy risk dataset used comes from the UCI Machine Learning Repository. Evaluation metrics used include accuracy, precision, recall, and F1-score. The results of the study revealed that the K-Means model with KNN provided the highest performance compared to the other two, with an accuracy of 79.53% and an average F1-score of 0.8. The implementation of K-Means resulted in an increase in accuracy of 0.4%, 1.57%, and 2.76% on KNN, SVM, and Naive Bayes respectively, which confirms the impact of clustering in improving classification performance. The resulting model can be used in real-time via a website built using the Flask API, and offers tools that can help health practitioners to plan treatments effectively and minimize the risk of pregnancy.
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H. B. Shulman, D. V. D’Angelo, L. Harrison, R. A. Smith and L. Warner, "The Pregnancy Risk Assessment Monitoring System (PRAMS): Overview of Design and Methodology," American Journal of Public Health, vol. CVIII, no. 10, pp. 1305-1313, 2018.
G. Stephen, M. Mgongo, T. H. Hashim, J. Katanga, B. Stray-Pedersen and S. E. Msuya, "Anaemia in Pregnancy: Prevalence, Risk Factors, and Adverse Perinatal Outcomes in Northern Tanzania," Anemia, pp. 1-9, 2018.
S. Sangin and C. Phonkusol, "Perception of Pregnancy Risk and Related Obstetric Factors among Women of Advanced Maternal Age," PRIJNR, vol. XXV, no. 3, pp. 494-500, 2021.
S. Lou, K. Carstensen, O. B. Petersen, C. P. Nielsen, L. Hvidman, M. R. Lanther and I. Vogel, "Termination of Pregnancy following a Prenatal Diagnosis of Down Syndrome: A Qualitative Study of the Decision-Making Process of Pregnant Couples.," Acta Obstetricia Et Gynecologica Scandinavica, vol. XCVII, no. 10, pp. 1228-1236, 2018.
J. A. Grieger, M. J. Hutchesson, S. D. Cooray, M. B. Khomami, S. Zaman, L. Segan, H. Teede and L. J. Moran, "A review of maternal overweight and obesity and its impact on cardiometabolic outcomes during pregnancy and postpartum," Therapeutic Advances in Reproductive Health, vol. XV, pp. 1-16, 2021.
Rokom, "Kemenkes Perkuat Upaya Penyelamatan Ibu dan Bayi," Kemenkes, 15 September 2021. [Online]. Available: https://sehatnegeriku.kemkes.go.id/baca/umum/20210914/3738491/kemenkes-perkuat-upaya-penyelamatan-ibu-dan-bayi/. [Accessed 25 May 2023].
S. Rajbanshi, M. N. Norhayati and N. H. Nik Hazlina, "High-risk pregnancies and their association with severe maternal morbidity in Nepal: A prospective cohort study," PLoS ONE, vol. XV, no. 12, pp. 1-14, 2020.
R. S. Kambli and Nirmala, "Model for Predicting Risk Levels in Maternal Healthcare," IJARIIE, vol. VIII, no. 6, pp. 1633-1637, 2022.
A. Singh, J. C. Mehta, D. Anand, P. Nath, B. Pandey and A. Khamparia, "An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k-means clustering and improved ensemble learning," Expert Systems, vol. XXXVIII, no. 1, pp. 1-13, 2021.
D. Zhao, X. Hu, S. Xiong, J. Tian, J. Xiang, J. Zhou and H. Li, "K-Means Clustering and KNN Classification Based on Negative Databases," Applied Soft Computing, vol. CX, pp. 1-15, 2021.
L. M. Abualigah, A. T. Khader and E. S. Hanandeh, "A Combination of Objective Functions and Hybrid Krill Herd Algorithm for Text Document Clustering Analysis," Engineering Applications of Artificial Intelligence, vol. LXXIII, pp. 111-125, 2018.
A. W. Syaputri, E. Irwandi and M. Mustakim, "NAÏVE BAYES ALGORITHM FOR CLASSIFICATION OF STUDENT MAJOR’S SPECIALIZATION," Journal of Intelligent Computing and Health Informatics, vol. I, no. 1, pp. 1-19, 2020.
L. Wang, Y. Zhang and Y. Rong, "Research on the Classification Algorithm for Cluster Analysis," In 2019 5th International Conference on Control, Automation and Robotics (ICCAR), pp. 469-474, 2019.
A. C. Khotimah and E. Utami, "Comparison Naïve Bayes Classifier, K-Nearest Neighbor And Support Vector Machine in The Classification of Individual on Twitter Account," Jurnal Teknik Informatika (JUTIF), vol. III, pp. 673-680, 2022.
K. L. Kohsasih and Z. Situmorang, "Analisis Perbandingan Algoritma C4.5 Dan Naïve Bayes Dalam Memprediksi Penyakit Cerebrovascular," JURNAL INFORMATIKA, vol. IX, no. 1, pp. 13-17, 2022.
F. Jiang, Y. Jiang and H. Zhi, "Evaluation metrics for classification models," Computational and mathematical methods in medicine, pp. 1-7, 2019.
S. Orozco-Arias, J. S. Piña, R. Tabares-Soto, L. F. Castillo-Ossa, R. Guyot and G. Isaza, "Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements," Processes, vol. VIII, no. 6, pp. 10-18, 2020.
C. K. P. Rukma and Alamsyah, "Increase Accuracy of Naïve Bayes Classifier Algorithm with K-Means Clustering for Prediction of Potential Blood Donors," Journal of Advances in Information Systems and Technology, vol. IV, no. 1, pp. 42-49, 2022.
M. &. Z. S. M. A. Aamir, "Clustering based semi-supervised machine learning for DDoS attack classification," Journal of King Saud University - Computer and Information Sciences, vol. XXXI, no. 3, pp. 377-384, 2019.
I. M. v. Hagen, E. Boersma, M. R. Johnson, S. A. Thorne, W. A. Parsonage, P. E. Subías, A. Leśniak-Sobelga, O. Irtyuga, K. A. Sorour, N. Taha, A. P. Maggioni, R. Hall and J. W. Roos-Hesselink, "Global cardiac risk assessment in the Registry Of Pregnancy And Cardiac disease: results of a registry from the European Society of Cardiology," European Journal of Heart Failure, vol. XVIII, pp. 523-533, 2016.
W. Vallejo, C. Díaz-Uribe and C. Fajardo, "Google Colab and Virtual Simulations: Practical e-Learning Tools to Support thes Teaching of Thermodynamics and to Introduce Coding to Students," ACS OMEGA, vol. VII, no. 8, pp. 7421-7429, 2022.
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