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

SN Computer Science volume 5, Article number: 23 (2024) Cite this article

Abstract

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.

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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.

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Authors and Affiliations

  1. Department of Physics, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India

    Arasada Subashini

  2. Department of Physics, GITAM School of Science, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India

    P. Kanaka Raju

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  1. Arasada Subashini
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  2. P. Kanaka Raju
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Correspondence to Arasada Subashini.

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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.

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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). https://doi.org/10.1007/s42979-023-02340-9

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