Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques

Priya, R. L.; Jinny, S. Vinila; Mate, Yash

doi:10.1007/s12553-020-00508-4

Cited by 23 publications

(9 citation statements)

References 24 publications

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“…663 by all authors. As additional selection criteria, [11], [15], [21], [28], [31], [37], [38] used F1 score, sensitivity, and specificity.…”

Section: Review Of the Articles On Cad Prediction Using Machine Learn...mentioning

confidence: 99%

“…With this combined DT from RF model, production rules are created, and this expert system diagnosed CAD victims very accurately at the rate of 92.04% in Nigeria. According to Jinny and Mate [21], it is evident that, of all the classifiers, by using the most features possible for the classification model, the RF classifier was successful in getting an accuracy of about 91%.…”

Section: Random Forestmentioning

confidence: 99%

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Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Vijayaraj,

Pasupathi

2024

IJ-AI

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<p>Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML-based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.</p>

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“…663 by all authors. As additional selection criteria, [11], [15], [21], [28], [31], [37], [38] used F1 score, sensitivity, and specificity.…”

Section: Review Of the Articles On Cad Prediction Using Machine Learn...mentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Vijayaraj,

Pasupathi

2024

IJ-AI

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“…Recent study 10‐12 presents that the latest technologies like Rapid and advanced learning models using artificial neural network, ensemble deep learning techniques helps to design the smart human care monitoring system and to improve the accuracy and reliability of heart disease prediction. Priya et al 13 applied a hyperparameter tuning methods such as TPOT 13 algorithm for the prediction system and compared the results with other models to achieve higher performance of the proposed model.…”

Section: Related Workmentioning

confidence: 99%

Internet of medical things‐based multitiered and hybrid architectural framework for effective heart disease prediction model

Priya

Jinny

2022

Concurrency and Computation

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Cardiovascular and chronic respiratory diseases are becoming a global threat. The alarming fact about Cardio-vascular diseases (CVD) is that rural areas are recording a high number of heart failures compared to urban areas. The proposed research work intends to create an effective medical setup in country-wide through Internet of Medical Things (IoMT)-based multitiered and hybrid architectural framework and Machine Learning (ML) model. The proposed architecture collects the medical data using the IoMT devices. During data preprocessing, categorical data are encoded using one-hot mechanism and is normalized using standard scalar technique. In addition, featureselection mechanisms are applied to choose the best significant features using hybrid sequence of Filter-Wrapper-Ensemble approach. To reduce the diagnostic barriers in health prediction model, an efficient method of Bayesian optimization for hyperparameter tuning and k-fold cross-validation resampling technique is applied to evaluate various conventional and ensemble ML techniques. The prediction results show the high accuracy of 86.47% and 85.81% using Logistic Regression and Extreme Gradient Boosting (XGBoosting) algorithm, respectively. In particular, the accuracy obtained using decision tree for the proposed approach is higher (80.53%) than that of existing approach (73.22%).

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“…Of these 26 studies, 11 (42%) used ML models [14,18,19,23,25,27,30,32,[34][35][36] and 3 (12%) used DL algorithms [20,31,38]. We observed that 12 studies incorporated both ML and DL models to analyze and validate different parameters [21,22,24,26,28,29,33,37,[39][40][41][42].…”

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review (Preprint)

Alamgir¹,

Mousa²,

3rd³

2021

Preprint

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BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

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Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques

Cited by 23 publications

References 24 publications

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Potential directions on coronary artery disease prediction using machine learning algorithms: A survey

Internet of medical things‐based multitiered and hybrid architectural framework for effective heart disease prediction model

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review (Preprint)

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