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Secure Predictive Analysis on Heart Diseases Using Partially Homomorphic Machine Learning Model

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Proceedings of International Joint Conference on Advances in Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Cardiovascular disease is the most important reason for death worldwide and significant public health distress. Timely prevention and treatment are possible by early prediction of the disease. However, there is necessary to include or vary new risk factors to improve the prediction models’ performance. It is one of the best ways to make development towards human-level AI. The machine learning (ML) algorithms PCA (Principal Component Algorithm) and XGboost classification are used to process the user queries and send the prediction. A major constraint is to secure the user queries submitted to the prediction models in order that the patient can submit their questions in an encrypted format to ensure security. It motivates us to develop a predictive model PHML which combines Partial Homomorphic Encryption and Machine Learning algorithms PCA and XGboost classification. The model is implemented in Amazon SageMaker with the dataset stored in Amazon S3 using the above algorithms. The patient query was submitted to the cloud with the encrypted format by the proposed partial homomorphic encryption algorithm. The machine learning algorithms predict the user queries, which are in encrypted form. The dataset includes multiple attributes like age, height, weight, gender, smoking, alcohol intake, physical activity, systolic blood pressure, cholesterol, glucose, diastolic blood pressure, and medical notes. With all features, doctors try to predict whether our individual has a high risk of cardiovascular.

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Abbreviations

\(\mathrm{ct}\) :

Ciphertext

\(\mathrm{g}\) :

Generator

\(\mathrm{p}\) :

Large prime number

\(\mathrm{pk},\mathrm{sk}\) :

Pk-public key, sk-secret key

\(\mathrm{pt}\) :

Plain text

\(\mathrm{r}1,\mathrm{r}2\) :

Random numbers

\({\mathrm{Z}}_{\mathrm{p}}^{*}\) :

Multiplicative group of a finite field

\((\mathrm{m},\mathrm{n})\) :

Ciphertext generated by encryption function

\(\uptheta \) :

\(\uptheta ={\upmu }^{\mathrm{g}}\mathrm{ mod}\,{\text{p}}\), Pre-computed parameter

\(\upmu \) :

Secret key

PCA:

Principal Component Algorithm

CVD:

Cardiovascular disease

gcd:

Greatest common divisor

PHML:

Partially Homomorphic Machine Learning

OW-CCA:

One Way-Chosen Ciphertext Attack

PRG:

Pseudorandom generator

PPT:

Probabilistic Polynomial Time

ML:

Machine Learning

AI:

Artificial Intelligence

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Authors and Affiliations

  1. Department of CSE, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

    M. D. Boomija & S. V. Kasmir Raja

Authors
  1. M. D. Boomija
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  2. S. V. Kasmir Raja
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Correspondence to M. D. Boomija .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Nazarbayev University, Nur-Sultan, Kazakhstan

    Prashant Kumar Jamwal

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Boomija, M.D., Kasmir Raja, S.V. (2022). Secure Predictive Analysis on Heart Diseases Using Partially Homomorphic Machine Learning Model. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_42

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