Abstract
The data mining methods are often beneficial in open domains like business, marketing, and retail. Healthcare is one of these areas that are still in its development phase. The healthcare industry is very rich in information, but sadly, not all data is carefully mined or discovered to draw out the effective, hidden patterns, and due to the lack of helpful analysis tools for discovering hidden relationships and trends in data, decision making is hampered (Dangare and Apte in Int J Comput Appl (0975–888) 47(10), 2012 [1]). There are a lot of advanced techniques in determining the domain that is used to discover knowledge from the healthcare database, particularly in diseases like heart disease, lung cancer, Parkinson’s disease, and others. This paper analyzes the predictions of heart disease on a dataset with 13 attributes. On the heart disease dataset, decision trees, Naive Bayes, support vector machine, linear discriminant analysis, logistic regression, and KNN are evaluated as data mining approaches (classification algorithms). Usually, the algorithms are compared based on their performance measure, and the algorithm with the highest accuracy is implemented on the heart dataset for the further model prediction.
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References
Dangare, C.S, Apte, S.S.: Improved study of heart disease prediction system using data mining classification techniques. Int. J. Comput. Appl. (0975–888), 47(10) (2012)
Gudadhe, M., Wankhade, K., Dongre, S.: Decision support system for heart disease based on support vector machine and artificial neural network. In: 2010 International Conference on Computer and Communication Technology (ICCCT), Allahabad, Uttar Pradesh, pp. 741–745 (2010)
Parthiban, L., Subramanian, R.: Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int. J. Med. Health Sci. 1(5) (2007)
Weng, S.F., Reps, J., Kai, J., Garibaldi, J.M., Qureshi, N.: Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12(4), e0174944 (2017). https://doi.org/10.1371/journal.pone.0174944
Aparna, K., Reddy, C.S., Prabha, I.S, Srinivas, K.V.: Disease prediction in data mining techniques. IJCST 5(2), 246–249 (2014)
Kamaraj, K.G., Priyaa, D.S.: Multi disease prediction using data mining techniques. Int. J. Syst. Soft. Eng. 4(2):12–14 (2016)
Ishaq, S., Sadiq, M., Umer, S., Ullah, S., Mirjalili, V., Rupapara, M.: Nappi, improving the prediction of heart failure patients’ survival using smote and effective data mining techniques,". IEEE Access 9, 39707–39716 (2021). https://doi.org/10.1109/ACCESS.2021.3064084
Vijiyarani, S., Sudha, S.: Disease prediction in data mining technique—a survey. Int. J. Comput. Appl. Inf. Technol. II(I), 17–21 (2013)
Chaves, L., Marques, G.: Data mining techniques for early diagnosis of diabetes: a comparative study. Appl. Sci. 11(5), 2218 (2021). https://doi.org/10.3390/app1105221
Rashid Abdulqadir, H., Mohsin Abdulazeez, A., Assad Zebari, D.: Data mining classification techniques for diabetes prediction. Qubahan Acad. J. 1(2), 125–133. https://doi.org/10.48161/qaj.v1n2a55
Diwakar, M., Tripathi, A., Joshi, K., et al.: Latest trends on heart disease prediction using machine learning and image fusion, Mater. Today: Proc. 37(4). https://doi.org/10.1016/j.matpr.2020.09.078
Lashari, S.A., Ibrahim, R., Senan, N., Taujuddin, N.: Application of data mining techniques for medical data classification: a review. pp. 737–759 (2018). https://doi.org/10.1007/978-981-15-3514-7_56
Wasan, S.K., Bhatnagar, V., Kaur, H.: The impact of data mining techniques on medical diagnostics. Data Sci. J. 5, 119–126 (2006). https://doi.org/10.2481/dsj.5.119
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: 2008 IEEE/ACS International Conference on Computer Systems and Applications, Doha, pp. 108–115 (2008). https://doi.org/10.1109/AICCSA.2008.4493524.
Shao, Y. E., Hou, C.-D., Chiu, C.-C.: Hybrid intelligent modeling, schemes for heart disease classification. Appl. Soft Comput. 14(Part A), 47–52 (2014)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Publication: VLDB ‘94: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Ordonez, C.: Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans. Inf. Technol. Biomed. 10(2), 334–343 (2006). https://doi.org/10.1109/TITB.2006.864475
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Sharma, N., Sarkar, D., Dutta, S. (2022). Disease Prediction Using Various Data Mining Techniques. In: Das, K.N., Das, D., Ray, A.K., Suganthan, P.N. (eds) Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6893-7_33
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DOI: https://doi.org/10.1007/978-981-16-6893-7_33
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