Skip to main content

An IoT-Based Heart Disease Diagnosis System Using Gradient Boosting and Deep Convolution Neural Network


The tremendous strides that have been made in biotechnology and the establishment of public healthcare infrastructures have resulted in a monumental increase in the generation of sensitive and important healthcare data. When intelligent data analysis methods are used, numerous fascinating patterns that may be used for the early and onset identification as well as the prevention of a number of illnesses that can be deadly are uncovered. Combining the results of many different classifiers, such as the Gradient Boosting Classifier and the Convolutional Neural Network (CNN), is the purpose of the ensemble learning approach known as the Voting Classifier, which is used in the detection of heart disease. The Gradient Boosting Classifier is a method of machine learning that is effective at managing structured or tabular data that have both numerical and category characteristics. It does this by successively integrating weak learners, such as decision trees, in order to recognize complicated patterns and correlations in patient health data in order to make correct predictions. This results in the construction of a robust model. In comparison, CNN is a technique to deep learning that is often used for problems involving image and signal processing. The proposed model obtained an accuracy of 92% while classifying the input data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Data availability

Data has been collected using Raspberry Pi 3 interface all sensors. The MLX90614IR temperature sensor is also used for temperature analysis at the time of heart disease. It is a non-contact sensor that can measure an object's temperature without making direct touch. The MLX90614 provides a practical solution for situations where direct touch temperature monitoring is not practical due to its capability to detect and convert infrared radiation into temperature data.


  1. Shani S, Majeed M, Alhassan S, Gideon A. Internet of things (IoTs) in the hospitality sector: challenges and opportunities. Adv Inform Commun Technol Comput Proc AICTC. 2022;2023:67–81.

    Google Scholar 

  2. Basheer S, Alluhaidan AS, Bivi MA. Real-time monitoring system for early prediction of heart disease using internet of things. Soft Comput. 2021;25(18):12145–58.

    Article  Google Scholar 

  3. Akhbarifar S, Haj Seyyed Javadi H, Rahmani AM, Hosseinzadeh M. A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment. Pers Ubiquit Comput. 2020;27(3):697–713.

    Article  Google Scholar 

  4. Gbadamosi B, Ogundokun RO, Adeniyi EA, Misra S, Stephens NF(2022) Medical data analysis for IoT-based datasets in the cloud using naïve bayes classifier for prediction of heart disease. In: Buyya R, Garg L, Fortino G, Misra S (eds) New frontiers in cloud computing and internet of things. Springer International Publishing, Cham, pp. 365––386

    Chapter  Google Scholar 

  5. Ramkumar G, Seetha J, Priyadarshini R, Gopila M, Saranya G. IoT-based patient monitoring system for predicting heart disease using deep learning. Measurement. 2023;218:113235.

    Article  Google Scholar 

  6. Rastogi R, Chaturvedi DK, Satya S, Arora N. Intelligent heart disease prediction on physical and mental parameters: a ML based IoT and big data application and analysis. In: Jain V, Chatterjee JM, editors. Machine learning with health care perspective: machine learning and healthcare. Cham: Springer International Publishing; 2020. p. 199–236.

    Chapter  Google Scholar 

  7. Kaustabh G, Karmakar A, Banerjee PS. ValveCare: a fuzzy based intelligent model for predicting heart diseases using arduino based IoT infrastructure. In: Buyya R, Garg L, Fortino G, Misra S, editors. International conference on computational intelligence in communications and business analytics. Cham: Springer International Publishing; 2021. p. 229–42.

    Google Scholar 

  8. Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R. HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Futur Gener Comput Syst. 2020;104:187–200.

    Article  Google Scholar 

  9. Khan MA, Algarni F. A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access. 2020;8:122259–69.

    Article  Google Scholar 

  10. Subahi AF, Khalaf OI, Alotaibi Y, Natarajan R, Mahadev N, Ramesh T. Modified self-adaptive Bayesian algorithm for smart heart disease prediction in IoT system. Sustainability. 2022;14(21):14208.

    Article  Google Scholar 

  11. Eisa MM, Alnaggar MH (2020) Hybrid rough-genetic classification model for IoT heart disease monitoring system. In: Magdi DA, Helmy YK, Mamdouh M, Joshi A (eds) Digital transformation technology: proceedings of ITAF. Springer, Singapore, pp 437–451

    Google Scholar 

  12. Al-Makhadmeh Z, Tolba A. Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: a classification approach. Measurement. 2019;147: 106815.

    Article  Google Scholar 

  13. Kishor A, Jeberson W (2021) Diagnosis of heart disease using internet of things and machine learning algorithms. In: Singh PK, Wierzchoń ST, Tanwar S, Ganzha M, Rodrigues JJPC (eds) Proceedings of second international conference on computing, communications, and cyber-security: IC4S 2020. Springer, Singapore, pp 691–702

    Google Scholar 

  14. Nisha R, Manocha AK. An IoT enabled secured clinical health care framework for diagnosis of heart diseases. Biomed Signal Process Control. 2023;80:104368.

    Article  Google Scholar 

  15. Keikhosrokiani P, Kamaruddin NSAB. IoT-Based in-hospital-in-home heart disease remote monitoring system with machine learning features for decision making. In: Mishra S, González-Briones A, Bhoi AK, Mallick PK, Corchado JM, editors. Connected e-health: integrated IoT and cloud computing. Cham: Springer International Publishing; 2022. p. 349–69.

    Chapter  Google Scholar 

Download references


No funding received for this research.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Arasada Subashini.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subashini, A., Raju, P.K. An IoT-Based Heart Disease Diagnosis System Using Gradient Boosting and Deep Convolution Neural Network. SN COMPUT. SCI. 5, 23 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: