基於腹瀉症狀的監測與響應系統，在支持向量機 (SVM) 的幫助下，根據用戶輸入的 6 種症狀預測可能的疾病，輸出是可能發生的疾病。我們也使用了另外兩個模型：隨機森林模型和朴素貝葉斯模型來做比較目的。 此外，根據水資源的地區性缺乏，對可能發生腹瀉爆發的地區進行了預測。 在支持向量機 接收平均值為100%準度，這就是為什麼它主要在系統中使用，其他兩個（隨機森林模型和朴素貝葉斯模型）不同，後者兩個模型在同一數據集上獲得97.62% 的平均準確度。

A surveillance and Response system based on symptoms of diarrhoea, with the help of the Support Vector Machine (SVM) to predict the probable Disease based on 6 symptoms that become an input from the user and the output is the disease which will likely occur. Two other models have been utilized, Random Forest Model and Naïve Bayes Model which are for comparison purposes. Furthermore, a prediction on the area in which a diarrhoea outbreak would likely occur based on the availability or the scarcity of water and the constituency in which the person is giving the symptoms is from. Support vector machine received an average of 100% which is why it will be used in the system unlike the other two (Random Forest Model and Naïve Bayes Model) who received a 97.62% average accuracy on the same dataset.

Bibliography

[1] Ogbo. A, Agho.K, Ogeleka. P. Woolfenden. S, Page. A, Eastwood. J.(2017). Prevalence and modifying effect of sanitation on infant and young child feeding indicators associated with diarrhoea in sub-Saharan African countries, with high burden of diarrhoea mortality. https://journals.plos.org/plosone/article?id=10.1371/ journal.pone.0171792
[2] Mlilo. P, Mhlanga. B, Senzanje. A. (2020). Emerging Issues in Water Resources Management in Swaziland. http://dlc.dlib.indiana.edu/dlc/bitstream/handle/ 10535/314/Mlilo_139401.pdf?sequence=1
[3] World Health Organization. (2017). Diarrhoeal Disease. http://www.who.int/ news-room/fact-sheets/detail/diarrhoeal-disease.
[4] Maphalala. G, Phungwayo. N, Masona. G, Lukhele. N, Tsegaye. G, Dube. N, Sindisiwe. D, Khumalo. L, Daniel. F, Katsande. R, Tate. E, Mwenda. M, Weldegebriel. G. (2018). Early impact of rotavirus vaccine in under 5year old children hospitalized due to diarrhea, Swaziland. Vaccine https://pubmed.ncbi.nlm.nih.gov/28778615/
[5] Mussumeci. E, Code¸co. F. (2020). Machine-learning forecasting for Dengue epidemics - Comparing LSTM, Random Forest and Lasso regression. https://www.medrxiv. org/content/10.1101/2020.01.23.20018556v1
[6] Abubakar. R, Olatunji. O. (2020). Computational intelligence-based model for diarrhea prediction using Demographic and Health Survey data. https://link. springer.com/article/10.1007%2Fs00500-019-04293-9
[7] Sacco. M, Farrugia. R, (2012). Driver fatigue monitoring system using Support Vector
Machines. pages:1-5. https://ieeexplore.ieee.org/document/6217754
[8] Ministry of Education.(2018). Eswatini Diarrhoea Contingency plan.
[9] Jordan, S.E.; Hovet, S.E.; Fung, I.C.-H.; Liang, H.; Fu, K.-W.; Tse, Z.T.H. (2018). Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response.
[10] Deepthi. Y, Kalyan. P, Vyas. M, Radhika.K, Babu. K, Krishna. V, (2020). Disease Prediction Based on Symptoms Using Machine Learning. https://papers.ssrn. com/sol3/papers.cfm?abstract_id=3661426
[11] Kanchan. B, Mahale. K. (2016). Study of machine learning algorithms for special disease prediction using principal of component analysis. pages 5-10. https: //ieeexplore.ieee.org/document/7955260
[12] Radhika. S, Ramiya. S, Rukhmani. V, Ranjitha. A. (2020). Symptoms Based Disease Prediction Using Decision Tree and Electronic Health Record Analysis. https:// ejmcm.com/article_1944.html
[13] Uddin. S, Khan. A, Hossain. M, Mohammad. M. (2019). Comparing different supervised machine learning algorithms for disease prediction.
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/ s12911-019-1004-8
[14] Corley. C, Pullum. L, Hartley. D, Benedum. C, Noonan. C, Rabinowitz. P, Lancaster. M. (2014). Disease Prediction Models and Operational Readiness. https: //journals.plos.org/plosone/article?id=10.1371/journal.pone.0091989
[15] Maphalala, G. et al. (2017). Prevalence of Rotavirus in Under 5 Year Old Children Hospitalized for Acute Gastroenteritis, Swaziland, 2013 – 2014. Page 353-
358. http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=
251&doi=10.11648/j.sjph.20170504.22
[16] Aishwarya. C, Suvarchala. K, Aravind. B, Shashank. G, Anand. M, Krishna. V. (2020). Prediction of Disease Using Machine Learning and Deep Learning https:
//link.springer.com/chapter/10.1007/978-981-15-5089-8_7
[17] Makadzange. K, Radebe. Z, Maseko. N. Lukhele. V, Masuku. S, Fakudze. G, Mengestu. K, Prasad, A. (2018). Implementation of Urban Health Equity Assessment and Response Tool: a Case of Matsapha, Swaziland https://link.springer. com/article/10.1007%2Fs11524-018-0241-y
[18] Sakkestad.T, Steinsland. H, Skrede.S, Lillebø. K,; Skutlaberg. H, Guttormsen. B, et al. (2019). Symptoms and signs other than diarrhea in 21 volunteers experimentally infected with ETEC strain TW10722.. PLOS Neglected Tropical Diseases https: //journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0007823
[19] Kavitha. R, Mohanapriya. N, Prabaharan. P. (2020). Disease Prediction Based on Symptoms Using Machine Learning Algorithm http://aegaeum.com/gallery/agm. j-2846.151-f.pdf
[20] Harini. D, Natesh. M. (2018). Prediction of Probability of Disease Based on Symptoms Using Machine Learning Algorithm https://www.irjet.net/archives/V5/ i5/IRJET-V5I574.pdf
[21] Chauhan. H, Naik. N, Halpati. A, Patel.J, Prajapati. A. (2020). Disease Prediction using Machine Learning https://www.irjet.net/archives/V7/i5/ IRJET-V7I5385.pdf
[22] Ghebrehewet. S, MacPherson. P, Ho, A. (2016). Influenza. BMJ (Clinical research ed.), 355, i6258. https://www.bmj.com/content/355/bmj.i6258
[23] Hung. M, Bounsanga. J, Voss. W. (2017). Interpretation of correlations in clinical research. Postgraduate medicine https://www.tandfonline.com/doi/full/10. 1080/00325481.2017.1383820
[24] Gurinder.S, Bhawna. K, Loveleen. G, Akriti. T. (2019). Comparison between Multinomial and Bernoulli Na¨ıve Bayes for Text Classification. https://www.researchgate.net/publication/334765873_Comparison_ between_Multinomial_and_Bernoulli_Naive_Bayes_for_Text_Classification
[25] Database Accessed date: 09-11-2020 https://www.kaggle.com/itachi9604/ disease-symptom-description-dataset?select=Symptom-severity.csv
[26] Finding Associated Concepts with Text Analysis Accessed date: 13-11-2020 http:
//www.nactem.ac.uk/facta/index.html
[27] Marouane. F. (2020). Disease Prediction Using Machine Learning. https:
//www.researchgate.net/publication/347381005_Disease_Prediction_
Using_Machine_Learning
[28] Zoabi. Y, Deri-Rozov.S, Shomron. N.(2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. https://www.nature.com/articles/ s41746-020-00372-6
[29] Sobasksa. N.(2021) Associations Between Improved Water Supply and Sanitation Usage and Childhood Diarrhea in Ethiopia: An Analysis of the 2016 Demographic and Health Survey. https://journals.sagepub.com/doi/pdf/10.1177/ 11786302211002552
[30] Abdulhamit. S, Alickovic.E, Kevric. J. (2017). Diagnosis of Chronic Kidney Disease by Using Random Forest. pages=589 -594. https://link.springer.com/chapter/ 10.1007/978-981-10-4166-2_89
[31] Muthukrishnan.T.(2018). Understanding the Classification report through sklearn Accessed:31-05-2021. https://muthu.co/
understanding-the-classification-report-in-sklearn/#:~:text=
A%20Classification%20report%20is%20used,predictions%20from%0a% 20classification%20algorithm.&text=The%20report%20shows%20the%20main, positives%2C%20true%20and%20false%20negatives.
[32] Shivam Kohli.(2019). Understanding a Classification Report For Your Machine Learning Model. Accessed:31-05-2021. https://medium.com/@kohlishivam5522/ understanding-a-classification-report-for-your-machine-learning-model-88815e2ce397
[33] Schober. P, Boer. C, Schwarte. L. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia Analgesia. https://www.researchgate.net/ publication/323388613_Correlation_Coefficients_Appropriate_Use_and_
Interpretation
[34] Joachims. T. (2002). Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. https://www.researchgate.net/publication/
257131900_Learning_to_Classify_Text_Using_Support_Vector_Machines_
Methods_Theory_and_Algorithms
[35] Grampurohit. S, Sagarnal.C.(2020). Disease Prediction using Machine Learning Algorithms pp. 1-7. https://ieeexplore.ieee.org/document/9154130