GAMA CUTE: Development of a Web-based for Gadjah Mada Caring University for Thalassemia Exit Prediction Tool by Applying Machine Learning
DOI:
https://doi.org/10.26555/jiteki.v10i3.29301Keywords:
Anemia, Classification, Machine Learning, Random Forest, K-Nearest Neighbor, GUIAbstract
Blood disorders occur in one or several parts of the blood that affect the nature and function, and blood disorders can be acute or chronic. Blood disease consists of several types, such as anemia. Anemia is the most common hematologic disorder associated with a decrease in the number of red blood cells or hemoglobin, causing a decrease in the ability of the blood to carry oxygen throughout the body. Patients with anemia in Indonesia have increased for the age of 15-24 years. This study aimed to conduct a screening test for anemia using machine learning. It is expected to know the process of knowing the type of anemia suffered. The machine learning technique used to identify the cause of anemia is divided into four classes, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination (Beta Thalassemia Trait and Iron Deficiency Anemia or Hemoglobin E and Iron Deficiency Anemia). This study would apply the K-Nearest Neighbor (KNN) and Random Forest (RF) methods to build a model on the data collected. The evaluation results using a confusion matrix in the form of accuracy, precision, recall, and f1-score against the KNN and RF methods are 79.36%, 59.40%, 62.80%, and 62.80%. In comparison, the RF is 87.30%, 90.89%, 78.40%, and 81.00%. From the results of comparing the two methods, the Graphic User Interface (GUI) implementation using python applies the RF method. The classifier that gets the highest value among all these parameters is called the best machine learning algorithm to perform screening tests for anemia.
References
[1] X. Han, C. Wang, and Z. Liu, “Red Blood Cells as Smart Delivery Systems,” Bioconjugate Chem., vol. 29, no. 4, pp. 852–860, Apr. 2018, https://doi.org/ 10.1021/acs.bioconjchem.7b00758.
[2] J. Amin et al., “An Integrated Design Based on Dual Thresholding and Features Optimization for White Blood Cells Detection,” IEEE Access, vol. 9, pp. 151421–151433, 2021, https://doi.org/ 10.1109/ACCESS.2021.3123256.
[3] H. Li et al., “Mechanics of diseased red blood cells in human spleen and consequences for hereditary blood disorders,” Proc Natl Acad Sci USA, vol. 115, no. 38, pp. 9574–9579, Sep. 2018, https://doi.org/ 10.1073/pnas.1806501115.
[4] C. Hézode et al., “Elbasvir/Grazoprevir for Patients With Hepatitis C Virus Infection and Inherited Blood Disorders: A Phase III Study: Hézode et al.,” Hepatology, vol. 66, no. 3, pp. 736–745, Sep. 2017, https://doi.org/ 10.1002/hep.29139.
[5] M. A. Ehsani, E. Shahgholi, M. S. Rahiminejad, F. Seighali, and A. Rashidi, “A new index for discrimination between iron deficiency anemia and beta-thalassemia minor: results in 284 patients.,” Pakistan journal of biological sciences : PJBS, vol. 12, no. 5, pp. 473–5, Mar. 2009, https://doi.org/ 10.3923/PJBS.2009.473.475.
[6] Y. Xu, T. Hu, H. Ding, and R. Chen, “Effects of anemia on the survival of patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis,” Expert Review of Respiratory Medicine, vol. 14, no. 12, pp. 1267–1277, 2020, https://doi.org/ 10.1080/17476348.2020.1816468.
[7] V. Laengsri, W. Shoombuatong, W. Adirojananon, C. Nantasenamart, V. Prachayasittikul, and P. Nuchnoi, “ThalPred: A web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1–14, 2019, https://doi.org/ 10.1186/s12911-019-0929-2.
[8] Badan Penelitian dan Pengembangan Kesehatan, “Laporan Nasional Riskesdas 2007,” Laporan Nasional 2007, pp. 1–384, 2007.
[9] Depkes RI., “Riset Kesehatan Dasar 2013,” 2013.
[10] Laporan Nasional Riskesdas, “Laporan_Nasional_RKD2018_FINAL.pdf,” 2018.
[11] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller, “Causability and explainability of artificial intelligence in medicine,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 4, pp. 1–13, 2019, https://doi.org/ 10.1002/widm.1312.
[12] Y. A. Jasim, “High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems,” ADCAIJ, vol. 10, no. 2, Feb. 2021, https://doi.org/ 10.14201/ADCAIJ202110297122.
[13] Y. Juhn and H. Liu, “Artificial intelligence approaches using natural language processing to advance EHR-based clinical research,” Journal of Allergy and Clinical Immunology, vol. 145, no. 2, pp. 463–469, Feb. 2020, https://doi.org/ 10.1016/j.jaci.2019.12.897.
[14] A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, and K. Shaalan, “Speech Recognition Using Deep Neural Networks: A Systematic Review,” IEEE Access, vol. 7, pp. 19143–19165, 2019, https://doi.org/ 10.1109/ACCESS.2019.2896880.
[15] D. C. E. Saputra, A. Azhari, and A. Ma”arif, “K-Nearest Neighbor of Beta Signal Brainwave to Accelerate Detection of Concentration on Student Learning Outcomes,” Engineering Letters, vol. 30, no. 1, pp. 318–234, 2022, [Online]. Available: http://www.engineeringletters.com/issues_v30/issue_1/EL_30_1_38.pdf
[16] G. Briganti and O. Le Moine, “Artificial Intelligence in Medicine: Today and Tomorrow,” Frontiers in Medicine, vol. 7, no. February, pp. 1–6, 2020, https://doi.org/ 10.3389/fmed.2020.00027.
[17] B. Rao, “Machine Learning Algorithms: A Review,” International Journal of Computer Science and Information Technologies, vol. 7, no. 3, pp. 1174–1179, 2016, https://doi.org/ 10.21275/ART20203995.
[18] R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions,” Sustainability, vol. 12, no. 2, p. 492, Jan. 2020, https://doi.org/ 10.3390/su12020492.
[19] H. Amakdouf, M. El Mallahi, A. Zouhri, A. Tahiri, and H. Qjidaa, “Classification and recognition of 3d image of charlier moments using a multilayer perceptron architecture,” Procedia Computer Science, vol. 127, pp. 226–235, 2018, https://doi.org/ 10.1016/j.procs.2018.01.118.
[20] N. Haefner, J. Wincent, V. Parida, and O. Gassmann, “Artificial intelligence and innovation management: A review, framework, and research agenda✰,” Technological Forecasting and Social Change, vol. 162, p. 120392, Jan. 2021, https://doi.org/ 10.1016/j.techfore.2020.120392.
[21] N. Mehta and M. V. Devarakonda, “Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?,” Journal of Allergy and Clinical Immunology, vol. 141, no. 6, pp. 2019-2021.e1, Jun. 2018, https://doi.org/ 10.1016/j.jaci.2018.02.025.
[22] S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review,” Chaos, Solitons & Fractals, vol. 139, p. 110059, Oct. 2020, https://doi.org/ 10.1016/j.chaos.2020.110059.
[23] C. González García, E. Núñez-Valdez, V. García-Díaz, C. Pelayo G-Bustelo, and J. M. Cueva-Lovelle, “A Review of Artificial Intelligence in the Internet of Things,” IJIMAI, vol. 5, no. 4, p. 9, 2019, https://doi.org/ 10.9781/ijimai.2018.03.004.
[24] C. Zhang and Y. Lu, “Study on artificial intelligence: The state of the art and future prospects,” Journal of Industrial Information Integration, vol. 23, p. 100224, Sep. 2021, https://doi.org/ 10.1016/j.jii.2021.100224.
[25] P. Vamplew, R. Dazeley, C. Foale, S. Firmin, and J. Mummery, “Human-aligned artificial intelligence is a multiobjective problem,” Ethics Inf Technol, vol. 20, no. 1, pp. 27–40, Mar. 2018, https://doi.org/ 10.1007/s10676-017-9440-6.
[26] J. Bartlett, “Generalized Information: A Straightforward Method for Judging Machine Learning Models,” cbi, vol. 1, no. 2, pp. 13–22, Jun. 2019, https://doi.org/ 10.33014/issn.2640-5652.1.2.bartlett.1.
[27] D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing, vol. 97, no. xxxx, p. 105524, 2020, https://doi.org/ 10.1016/j.asoc.2019.105524.
[28] C. Lin, D. Wu, H. Liu, X. Xia, and N. Bhattarai, “Factor identification and prediction for teen driver crash severity using machine learning: A case study,” Applied Sciences (Switzerland), vol. 10, no. 5, 2020, https://doi.org/ 10.3390/app10051675.
[29] T. R. Gadekallu et al., “Early detection of diabetic retinopathy using PCA-firefly based deep learning model,” Electronics (Switzerland), vol. 9, no. 2, pp. 1–16, 2020, https://doi.org/ 10.3390/electronics9020274.
[30] N. Z. A. Halim, S. A. Sulaiman, K. Talib, and M. N. Isa, “Assessing the usability of the NDCDB checklist with Systematic Usability Scale (SUS),” IOP Conf. Ser.: Earth Environ. Sci., vol. 169, p. 012099, Jul. 2018, https://doi.org/ 10.1088/1755-1315/169/1/012099.
[31] E. M. T. El-kenawy, “A Machine Learning Model for Hemoglobin Estimation and Anemia Classification Anemia Classification Module Hemoglobin Estimation Module Data Cleaning Data Preprocessing,” International Journal of Computer Science and Information Security (IJCSIS), vol. 17, no. 2, pp. 100–108, 2019,
[32] J. R. Khan, S. Chowdhury, H. Islam, and E. Raheem, “Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh,” Journal of Data Science, vol. 17, no. 1, pp. 195–218, 2021, https://doi.org/ 10.6339/jds.201901_17(1).0009.
[33] M. Jaiswal, A. Srivastava, and T. J. Siddiqui, Machine learning algorithms for anemia disease prediction, vol. 524. Springer Singapore, 2019. https://doi.org/ 10.1007/978-981-13-2685-1_44.
[34] B. Sow, H. Mukhtar, H. F. Ahmad, and H. Suguri, “Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques,” Informatics for Health and Social Care, vol. 45, no. 3, pp. 229–241, 2020, https://doi.org/ 10.1080/17538157.2019.1582056.
[35] M. M. Eid and A. Ibrahim, “Anemia Estimation for COVID-19 Patients Using A Machine Learning Model,” Journal of Computer Science and Information Systems, vol. 17, no. 11, pp. 2535-1451, 2021, https://www.semanticscholar.org/paper/Anemia-Estimation-for-COVID-19-Patients-Using-A-El-kenawy-Eid/e1a71f96005c7790d6112d5933b81369b6676559.
[36] H. A. Aliyu, M. A. A. Razak, R. Sudirman, and N. Ramli, “A deep learning alexnet model for classification of red blood cells in sickle cell anemia,” IAES International Journal of Artificial Intelligence, vol. 9, no. 2, pp. 221–228, 2020, https://doi.org/ 10.11591/ijai.v9.i2.pp221-228.
[37] S. Kilicarslan, M. Celik, and Ş. Sahin, “Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification,” Biomedical Signal Processing and Control, vol. 63, 2021, https://doi.org/ 10.1016/j.bspc.2020.102231.
[38] S. Yeruva, M. Sharada Varalakshmi, B. Pavan Gowtham, Y. Hari Chandana, and P. E. S. N. Krishna Prasad, “Identification of sickle cell anemia using deep neural networks,” Emerging Science Journal, vol. 5, no. 2, pp. 200–210, 2021, https://doi.org/ 10.28991/esj-2021-01270.
[39] S. Purwar, R. Tripathi, R. Ranjan, and R. Saxena, “Classification of Thalassemia Patients Using a Fusion of Deep Image and Clinical Features,” in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India: IEEE, Jan. 2021, pp. 410–415. https://doi.org/ 10.1109/Confluence51648.2021.9377054.
[40] H. Luo, X. Pan, Q. Wang, S. Ye, and Y. Qian, “Logistic Regression and Random Forest for Effective Imbalanced Classification,” in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, WI, USA: IEEE, Jul. 2019, pp. 916–917. https://doi.org/ 10.1109/COMPSAC.2019.00139.
[41] M. Zhu et al., “Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data,” IEEE Access, vol. 6, pp. 4641–4652, 2018, https://doi.org/ 10.1109/ACCESS.2018.2789428.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Dimas Chaerul Ekty Saputra, Afiahayati Afiahayati, Tri Ratnaningsih
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License
