Abstract
Meticulous and prompt analysis of any health related issues is significant for the anticipation and treatment of the disease. The conventional method of determination may not be adequate on account of a genuine infirmity. Fostering a clinical determination framework dependent on machine learning calculations for forecast of any illness can help in a more exact finding than the regular strategy. We have built a disease predication framework utilizing numerous machine learning techniques from symptoms. The dataset utilized had more than 261 illnesses and 500 symptoms for handling. The Random Forest Classifier gave the best outcomes when contrasted with Multinomial Naïve Bayes Classifier, K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Decision Tree, and Multilayer Perceptron Classifier models. The accuracy of the proposed Random Forest Classifier model on the given dataset was 91.06%. Our prediction model can go about as a specialist for the early finding of disease to guarantee the treatment can happen on schedule and lives can be saved.
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References
Karayılan T, Kiliç Ö (2017) Prediction of heart disease using neural network. In: 2017 international conference on computer science and engineering (UBMK), pp 719–723
Chae S, Kwon S, Lee D (2018) Predicting infectious disease using deep learning and big data. Int J Environ Res Public Health 15(8):1596. https://doi.org/10.3390/ijerph15081596.PMID:30060525;PMCID:PMC6121625
Haq AU, Li JP, Memon MH, Nazir S, Sun R (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Inf Syst Article ID 3860146, pp 21
Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707
Maniruzzaman M, Rahman MJ, Ahammed B, Abedin MM (2020) Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst 8(1):7. https://doi.org/10.1007/s13755-019-0095-z.PMID:31949894;PMCID:PMC6942113
Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS (2021) Heart disease prediction using hybrid machine learning model. In: 2021 6th international conference on inventive computation technologies (ICICT), pp 1329–1333. https://doi.org/10.1109/ICICT50816.2021.9358597.
Langbehn DR, Brinkman RR, Falush D, Paulsen JS, Hayden MR (2001) International Huntington’s disease collaborative group. A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin Genet 65(4):267–77
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17, ISSN 2001–0370
Monto AS, Gravenstein S, Elliott M, Colopy M, Schweinle J (2000) Clinical signs and symptoms predicting influenza infection. Arch Intern Med 160(21):3243–3247. https://doi.org/10.1001/archinte.160.21.3243 PMID: 11088084
Chen M et al (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5 (2017):8869–8879
Haq AU et al (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob Inf Syst 3860146:1–3860146:21
Mir A, Dhage SN (2018) Diabetes disease prediction using machine learning on big data of healthcare. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA), pp 1–6
Khourdifi Y, Bahaj M (2019) Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. Int J Intell Eng Syst
Vijayarani S, Dhayanand S (2015) Liver disease prediction using SVM and Naïve Bayes algorithms
Sriram TV, Rao MV, Narayana GS, Kaladhar D, Vital TPR (2013) Intelligent Parkinson disease prediction using machine learning algorithms. Int J Eng and Innov Technol (IJEIT) 3(3):1568
Battineni G et al (2020) Applications of machine learning predictive models in the chronic disease diagnosis. J Personalized Med 10(2):21. https://doi.org/10.3390/jpm10020021
Alotaibi FS (2019) Implementation of machine learning model to predict heart failure disease. Int J Adv Comput Sci Appl (IJACSA), 10(6)
Bindhika GSS, Meghana M et al (2020) Heart disease prediction using machine learning techniques. Int Res J Eng Technol 7(4):5272–5276
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Shah, D.R., Dhawan, D.A. (2023). Disease Prediction Based on Symptoms Using Various Machine Learning Techniques. In: Buyya, R., Hernandez, S.M., Kovvur, R.M.R., Sarma, T.H. (eds) Computational Intelligence and Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-19-3391-2_10
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DOI: https://doi.org/10.1007/978-981-19-3391-2_10
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