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Cardiac disease prediction using AI algorithms with SelectKBest

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Abstract

Atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease (CHD) and ischemic stroke, is the leading cause of mortality globally. According to the European Society of Cardiology (ESC), 26 million people worldwide have heart disease, with 3.6 million diagnosed each year. Early detection of heart disease will aid in lowering the mortality rate. The lack of diversity in training data and the difficulty in comprehending the findings of complicated AI models are the key issues in current research for heart disease prediction using artificial intelligence. To overcome this, in this paper, cardiac disease prediction using AI algorithms with SelectKBest has been proposed. Features are standardized, balanced, and selected using the StandardScaler, SMOTE, and SelectKBest techniques. Machine learning models such as support vector machine (SVM), K-nearest neighbor(KNN), decision tree (DT), logistic regression (LR), adaptive boosting (AB), naive Bayes (NB), random forest (RF), and extra tree (ET) and deep learning models such as vanilla long short-term memory (LSTM), bidirectional long short-term memory (LSTM), stacked long short-term memory (LSTM), and deep neural network (DNN) are assessed using Alizadeh Sani, combined (Cleveland, Hungarian, Switzerland, Long Beach VA, and Stalog), and Pakistan heart failure datasets. As a result of the evaluation, the proposed deep neural network (DNN) with SelectKBest predicted heart disease in a promising way. The prediction rate of unweighted accuracy of 99% on Alizadeh Sani, 98% on combined, and 97% on Pakistan are gained in tenfold cross-validation experiments. The suggested approach can be utilized to diagnose heart disease in its early stages.

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Abbreviations

ASCVD :

Atherosclerotic cardiovascular disease

CHD :

Coronary heart disease

ESC :

European Society of Cardiology

Region RWMA :

Regional wall motion abnormality

HTN :

Hypertension

DM :

Diabetes mellitus

BP :

Blood pressure

EF-TTE :

Ejection fraction-transthoracic echocardiography

FBS :

Fasting blood sugar

K :

Potassium

ESR :

Erythrocyte sedimentation rate

PR :

Pulse rate

Q Wave :

Are both abnormally deep and wide implying myocardial infarction

TG :

Triglyceride

Lymph :

Lymphocyte

Neut :

Neutrophil

Poor R progression :

Poor R-wave progression

PLT :

Platelet

CRF :

Chronic renal failure

BUN :

Blood urea nitrogen

CR :

Creatine

Na :

Sodium

BMI :

Body mass index

WBC :

White blood cell

ST slope :

The slope of the peak exercise ST segment

oldpeak :

ST depression induced by exercise relative to rest

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Authors and Affiliations

  1. College of Basic Education, University of Halabja, Halabja, 46018, Iraq

    Mariwan Hama Saeed

  2. Research Center, University of Halabja, Halabja, 46018, Iraq

    Jihad Ibrahim Hama

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  1. Mariwan Hama Saeed
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Contributions

The introductory section of the paper was prepared by Jihad Hama, who also reviewed the initial draft of the paper. Mariwan, for their part, was instrumental in the creation of the remainder of the paper’s written content and additionally spearheaded the practical aspects of the study. Moreover, Mariwan took on the task of acquiring, collating, preparing, scrutinizing, and evaluating the paper’s dataset.

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Correspondence to Mariwan Hama Saeed.

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Appendix

Appendix

Table 10

Table 10 Recall and precision scores of the proposed model for the used datasets
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Saeed, M.H., Hama, J.I. Cardiac disease prediction using AI algorithms with SelectKBest. Med Biol Eng Comput 61, 3397–3408 (2023). https://doi.org/10.1007/s11517-023-02918-8

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