WITHDRAWN: Heart disease and optimal prediction of attacks using hybrid machine learning algorithm: A survey

Sridhar, G.; Kirubakaran, A. Prema

doi:10.1016/j.matpr.2020.12.865

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Further, three ML methods, namely, SVM, multi-layer perception neural networks, decision trees and ensemble methods for improving the accuracy of the models (Latha and Jeeva, 2019) were investigated to predict CVD, searching the space of hyperparameters for each method (Faiayaz Waris and Koteeswaran, 2021) (Hagan et al , 2021). Hybrid ML techniques are used for the construction of CVD prediction (Sridhar and Prema Kirubakaran, 2021). An overview of the integration of IoT and ML approach was also discussed in Ganesan and Sivakumar (2019), Gupta et al (2019); He et al (2020), where a patient can be monitored continuously for early disease prediction.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques

Ahamed

Mir

Chishti

2022

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Purpose The world is shifting towards the fourth industrial revolution (Industry 4.0), symbolising the move to digital, fully automated habitats and cyber-physical systems. Industry 4.0 consists of innovative ideas and techniques in almost all sectors, including Smart health care, which recommends technologies and mechanisms for early prediction of life-threatening diseases. Cardiovascular disease (CVD), which includes stroke, is one of the world’s leading causes of sickness and deaths. As per the American Heart Association, CVDs are a leading cause of death globally, and it is believed that COVID-19 also influenced the health of cardiovascular and the number of patients increases as a result. Early detection of such diseases is one of the solutions for a lower mortality rate. In this work, early prediction models for CVDs are developed with the help of machine learning (ML), a form of artificial intelligence that allows computers to learn and improve on their own without requiring to be explicitly programmed. Design/methodology/approach The proposed CVD prediction models are implemented with the help of ML techniques, namely, decision tree, random forest, k-nearest neighbours, support vector machine, logistic regression, AdaBoost and gradient boosting. To mitigate the effect of over-fitting and under-fitting problems, hyperparameter optimisation techniques are used to develop efficient disease prediction models. Furthermore, the ensemble technique using soft voting is also used to gain more insight into the data set and accurate prediction models. Findings The models were developed to help the health-care providers with the early diagnosis and prediction of heart disease patients, reducing the risk of developing severe diseases. The created heart disease risk evaluation model is built on the Jupyter Notebook Web application, and its performance is calculated using unbiased indicators such as true positive rate, true negative rate, accuracy, precision, misclassification rate, area under the ROC curve and cross-validation approach. The results revealed that the ensemble heart disease model outperforms the other proposed and implemented models. Originality/value The proposed and developed CVD prediction models aims at predicting CVDs at an early stage, thereby taking prevention and precautionary measures at a very early stage of the disease to abate the predictive maintenance as recommended in Industry 4.0. Prediction models are developed on algorithms’ default values, hyperparameter optimisations and ensemble techniques.

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