Abstract
Heart Disease is one of the serious diseases in the world. So, there is a huge requirement for early prediction and diagnosis of heart disease. Deep Learning is an emerging technology used widely in Health Care sector. In this research work, a sequential neural network model is used with corresponding parameters to develop heart disease prediction system. The performance of a model is assessing with different metrics like Accuracy, Precision, Recall and F1-Score. As values of parameters has effect on the performance of a model so choosing best optimal values is necessary for improvement. In this regarding, Hyper parameter tuning is used. For all parameters, every possible value will be tried to create a model and then their performance is evaluated. Manually, this evaluation is not that much effective and even become tedious and complex if number of parameters are high. So Gridsearchcv approach is used to tune parameters of model to get best set of values. Then these tuned values are used to create a model and then tested on test dataset. Finally model performance is evaluated with respect to corresponding evaluation metrics. From the evaluated results, it is observed that a model with Hyper parameter tuning induced highest accuracy of 83.60 than a model without Hyper parameter tuning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sajja TK, Kalluri HK (2020) A deep learning method for prediction of cardiovascular disease using convolutional neural network. Revue d'Intelligence Artificielle 34(5):601–606. https://doi.org/10.18280/ria.340510
Pasha SN et al (2020) Cardiovascular disease prediction using deep learning techniques. IOP Conf Ser Mater Sci Eng 981:022006
Sharma V, Rasool A, Hajela G (2020) Prediction of heart disease using DNN. In: 2020 second international conference on inventive research in computing applications (ICIRCA), pp 554-562. https://doi.org/10.1109/ICIRCA48905.2020.9182991
Mehmood A, Iqbal M, Mehmood Z et al (2021) Prediction of heart disease using deep convolutional neural networks. Arab J Sci Eng 46:3409–3422. https://doi.org/10.1007/s13369-020-05105-1
Rajamhoana SP, Devi CA, Umamaheswari K, Kiruba R, Karunya K, Deepika R (2018) Analysis of neural networks based heart disease prediction system. In: 2018 11th international conference on human system interaction (HSI), pp 233–239. https://doi.org/10.1109/HSI.2018.8431153
Shankar V, Kumar V, Devagade U et al (2020) Heart disease prediction using CNN algorithm. SN Comput Sci 1:170. https://doi.org/10.1007/s42979-020-0097-6
Kim JK, Kang S (2017) Neural network-based coronary heart disease risk prediction using feature correlation analysis. J Healthcare Eng 13, Article ID 2780501. https://doi.org/10.1155/2017/2780501
Dangare C, Apte S (2012) A data mining approach for prediction of heart disease using neural networks. Int J Comput Eng Technol 3(3)
Javid I, Zager A, Ghazali R (2020) Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int J Adv Comput Sci Appl 11(3):110369. https://doi.org/10.14569/IJACSA.2020.0110369
Mantovani RG, Horváth T, Cerri R, Vanschoren J, de Carvalho ACPLF (2017) Hyper-parameter tuning of a decision tree induction algorithm. In: 5th Brazilian conference on intelligent systems, BRACIS 2016, Recife, Pernambuco, Brazil, pp 37–42, 9 October 2016–12 October 2016. Institute of Electrical and Electronics Engineers, Piscataway
Sonth MV, Ambesange S, Sreekanth D, Tulluri S (2020) Optimization of random forest algorithm with ensemble and hyper parameter tuning techniques for multiple heart diseases, 27 November 2020. https://doi.org/10.13140/RG.2.2.12451.68649
Soares de Andrades R, Grellert M, Beck Fonseca M (2019) Hyperparameter tuning and its effects on cardiac arrhythmia prediction. In: 2019 8th Brazilian conference on intelligent systems (BRACIS), pp 562–567. https://doi.org/10.1109/BRACIS.2019.00104
Ambesange S, Vijayalaxmi A, Sridevi S, Venkateswaran, Yashoda BS (2020) Multiple heart diseases prediction using logistic regression with ensemble and hyper parameter tuning techniques. In: 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4), pp 27–832. https://doi.org/10.1109/WorldS450073.2020.9210404
Priya RL, Jinny SV, Mate YV (2021) Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques. Health Technol 11:63–73. https://doi.org/10.1007/s12553-020-00508-4
Asvinth A, Hiremath M (2020) A computational model for prediction of heart disease based on logistic regression with GridSearchCV. Int J Sci Technol Res 9(03). ISSN 2277-8616
Gupta S, Sedamkar RR (2021) Genetic algorithm for feature selection and parameter optimization to enhance learning on Framingham heart disease dataset. In: Balas VE, Semwal VB, Khandare A, Patil M (eds) Intelligent Computing and Networking. LNNS, vol 146. Springer, Singapore. https://doi.org/10.1007/978-981-15-7421-4_2
Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innov Technol Exploring Eng (IJITEE) 9(3). ISSN 2278-3075
Kayiram K, Laxman Kumar S, Pravallika P, Sruthi K, Lalitha RVS, Krishna Rao NV (2020) Fashion compatibility, recommendation system, convolutional neural networks, sentiment analysis. In: International conference, ACCES 2020, GRIET, Hyderabad, 18th and 19th September 2020
Lalitha RVS, Divya Lalitha Sri J, Kavitha K, Rayudu Srinivas RRT, Sujana C (2021) Prediction and analysis of corona virus disease (COVID-19) using Cubist and OneR. IOP Conf Ser Mater Sci Eng 1074:012022. https://doi.org/10.1088/1757-899X/1074/1/012022
Nawaz MS, Shoaib B, Ashraf MA (2021) Intelligent cardiovascular disease prediction empowered with gradient descent optimization. Heliyon 7(5):e06948. https://doi.org/10.1016/j.heliyon.2021.e06948. PMID: 34013084, PMCID: PMC8113842
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jalligampala, D.L.S., Lalitha, R.V.S., Ramakrishnarao, T.K., Mylavarapu, K.R., Kavitha, K. (2022). Efficient Classification of Heart Disease Forecasting by Using Hyperparameter Tuning. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-4831-2_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4830-5
Online ISBN: 978-981-19-4831-2
eBook Packages: Computer ScienceComputer Science (R0)