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Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning

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Abstract

The rapid increase of the aged population and challenges towards taking health care and social care become the key point for the industry and researchers nowadays. Heart diseases are typical chronic illnesses with a high recurrence rate. In some of the cases, a heart attack occurs suddenly without any omens. Patients typically live in their homes rather than in hospitals and are often unable to access medical care in an emergency. Cardiovascular disease leads to a significant difficulty for the doctors to know the patient’s status in time, and it becomes one of the significant reasons for death. To overcome these problems, a solution needs to design, implement, and validate adequately through an appropriate base knowledge. To overcome these challenges, remotely real-time patient’s health data can be identified. Today Internet of Things is playing a key role in solving the problem of heart disease. The patients can avail of the medical resource much. This research work aims to propose a framework for prediction of heart disease using major risk factors based on various classifier arrangements; K-nearest neighbors, Naïve Bayes, support vector machine, Lasso and ridge regression algorithms. Apart from these data classification, linear discriminant analysis and principal component analysis were done. The support vector machine provides 92% accuracy, and F1 accuracy is 85%. The performance of the proposed research work is evaluated using precision, accuracy, and sensitivity.

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Abbreviations

IoT:

Internet of Things

HIoT:

Healthcare Internet of Things

ML:

Machine learning

AI:

Artificial intelligence

ANN:

Artificial neural network

KNN:

K-nearest neighbors

NB:

Naïve Bayes

SVM:

Support vector machine

SSVM:

Smooth support vector machine

LRR:

Lasso and ridge regression

LDA:

Linear discriminant analysis

PCA:

Principal component analysis

WSN:

Wireless sensor networks

WWSN:

Wearable wireless sensor network

WBAN:

Wireless body area network

SW-SHMS:

Wearable Sensors for Smart Healthcare Monitoring System

AAL:

Ambient assisted living

CVD:

Cardiovascular disease

CART:

Cardiac regenerative therapy

PSO:

Particle swarm optimization

HRFLM:

Hybrid Random Forest with a Linear Model

PUAI:

Primary use of AI

ABC:

Artificial bee colony

ANFIS:

Adaptive network-based fuzzy inference system 

FNN:

Feed-forward networks

MIFH:

Machine intelligence framework for diagnosis heart disease

FAMD:

Factor analysis of mixed data

CHD:

Coronary heart disease

CVD:

Cardiovascular disease

AUC:

Area under the roc curve

PLSM:

Partial least square method

HRV:

Heart rate variability

DT:

Decision tree

CNN:

Conventional neural network

WBC:

White blood cell

SESSA:

Statistically enhanced scalp swarm algorithm

DSS:

Decision support systems

WHO:

World Health Organization

DA:

Data analysis

RFID:

RF identification

BGM:

Blood glucose monitoring

SCA:

Sudden cardiac arrest

IPDA:

Intelligent personal digital assistant

ORP:

Optoelectronic retina prosthesis

SHMS:

Smart Healthcare Monitoring System

WS-SHMS:

Wearable Sensor for Smart Healthcare Monitoring System

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur, India

    Sibo Prasad Patro, Neelamadhab Padhy & Dukuru Chiranjevi

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  1. Sibo Prasad Patro
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Patro, S.P., Padhy, N. & Chiranjevi, D. Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning. Evol. Intel. 14, 941–969 (2021). https://doi.org/10.1007/s12065-020-00484-8

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