Abstract
Machine learning applications in health science become more important and necessary every day. With the help of these systems, the load of the medical staff will be lessened and faults because of a missing point, or tiredness will decrease. It should not be forgotten that the last decision lies with the professionals, and these systems will only help in decision-making. Predicting diseases with the help of machine learning algorithm can lessen the load of the medical staff. This paper proposes a machine learning model that analyzes healthcare data from a variety of diseases and shows the result from the best resulting algorithm in the model. It is aimed to have a system that facilitates the diagnosis of diseases caused by the density of data in the health field by using these algorithms of previously diagnosed symptoms, thus resulting in doctors going a faster way while diagnosing the disease and have a prediction about the diseases of people who do not have the condition to go to the hospital. In this way, it can ease the burden on health systems. The disease outcome corresponding to the 11 symptoms found in the data set used is previously experienced results. During the study, different ML algorithms such as Decision Tree, Random Forest, KNN, XGBoost, SVM, LDA were tried and compatibility/performance comparisons were made on the dataset used. The results are presented in a table. As a result of these comparisons and evaluations, it was seen that Random Forest Algorithm gave the best performance. While data was being processed, input parameters were provided to each model, and disease was taken as output. Within this limited resource, our model has reached an accuracy rate of 98%.
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References
Akhtar, N.: Heart Disease Prediction (2021)
Jany Shabu, S.L., Nithin, M.S., Santhosh, M., Roobini, M.S., Mohana Prasad, K., Joshila Grace, L.K.: Skin disease prediction. J. Comput. Theor. Nanosci. 17(8), 3458–3462 (2020)
Shilimkar, G., Shivam, P.: Disease prediction using machine learning. Int. J. Sci. Res. Sci. Technol. 8(3), 551–555 (2021)
Tamal, M.A., Islam, M.S., Ahmmed, M.J., Aziz, M.A., Miah, P., Karim, M.R.: Heart disease prediction based on external factors: a machine learning approach. Int. J. Adv. Comput. Sci. Appl. 10 (2019) https://doi.org/10.14569/IJACSA.2019.0101260
Rajora, H., Punn, N.S., Sonbhadra, S.K., Agarwal, S.: Web based disease prediction and recommender system (2021)
John, R.: An application of machine learning in IVF: comparing the accuracy of classification algorithms for the prediction of twins. Gynecol. Obstet. 9(497), 0932–2161 (2019). https://doi.org/10.4172/2161-0932.1000497
Lee, R., Chitnis, C.: Improving health-care systems by disease prediction. In: 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 726–731 (2018). https://doi.org/10.1109/CSCI46756.2018.00145
Shetty, S.V., Karthik, G.A., Ashwin, M.: Symptom based health prediction using data mining. In: 2019 International Conference on Communication and Electronics Systems (ICCES), pp. 744–749 (2019). https://doi.org/10.1109/ICCES45898.2019.9002132
Joshi, T.N., Chawan, P.M.: Logistic regression and SVM based diabetes prediction system. Int. J. Technol. Res. Eng. 5, 4347–4350 (2018)
Lafta, R., Zhang, J., Tao, X., Li, Y., Tseng, V.S.: An intelligent recommender system based on short-term risk prediction for heart disease patients. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 3, pp. 102–105. IEEE (2015). https://doi.org/10.1109/WI-IAT.2015.47
Baig, M., Nadeem, M.: Diabetes prediction using machine learning algorithms (2020). https://doi.org/10.13140/RG.2.2.18158.64328
https://www.kaggle.com/itachi9604/disease-symptom-description-dataset
Mujumdar, A., Vaidehi, V.: Diabetes prediction using machine learning algorithms. Int. Conf. Recent Trends Adv. Comput. ICRTAC 165, 292–299 (2019)
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Meriç, E., Özer, Ç. (2023). Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds) Computational Intelligence, Data Analytics and Applications. ICCIDA 2022. Lecture Notes in Networks and Systems, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-27099-4_15
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