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Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques | IEEE Conference Publication | IEEE Xplore

Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques


Abstract:

Heart disease (HD) is a concerning health condition that demands immediate attention. This article employs three artificial intelligence classification algorithms: DT, NB...Show More

Abstract:

Heart disease (HD) is a concerning health condition that demands immediate attention. This article employs three artificial intelligence classification algorithms: DT, NB, and SGD to address this issue. The primary goal is to determine the most effective algorithm for early-stage prediction of heart disease, using easily accessible patient information. A dataset of 290 simple input variables, including sex, age, ??? blood sugar, fasting blood sugar levels, and maximal heart rate, was collected from a hospital in Armenia. The results reveal that the DT algorithm outperforms NB and SGD in accuracy, with CAtrain of 0.96 and CAtest of 0.93. Accurate early prediction enables timely interventions and support for patients, improving healthcare management. Leveraging artificial intelligence, particularly DT, enables swift identification of potential heart disease cases, aiding healthcare professionals in providing essential care and preventive measures. Furthermore, the pragmatic approach of employing easily accessible patient data for prediction underscores the feasibility and applicability of the proposed methodology. By relying on readily available and cost-effective information, medical practitioners can efficiently screen and diagnose individuals at risk of heart disease, leading to improved healthcare management and better patient outcomes.
Date of Conference: 19-20 October 2023
Date Added to IEEE Xplore: 07 February 2024
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Conference Location: Budapest, Hungary

I. Introduction

One of the diseases that has been consistently prevalent in the field of medicine throughout history, and continues to be so, is heart disease (HD) [1]. The occurrence of this disease is attributed to various factors, with the most significant ones being stress, sleepless, high cholesterol diet, sedentary lifestyle, and prolonged periods of sadness, [2]. There are specific types of HD, including arterial hypertension and coronary artery disease (CAD), which the World Health Organization (WHO) has identified as the most common heart ailments [3], [4]. To predict CAD, numerous tests are conducted. However, these tests can be time-consuming and sometimes fail to deliver the desired efficiency, leading to more serious issues for the patients [5]. It is crucial to raise awareness about the risk factors associated with HD and adopt preventive measures to mitigate its impact on individuals and society [6]. A healthy lifestyle, regular exercise, balanced diet, and stress management can significantly reduce the risk of HD development [7]. Moreover, advances in medical technology and research can help improve diagnostic procedures, allowing for earlier detection and better management of heart conditions [8]. The old methods for diagnosing and predicting HD have long been associated with significant time and financial burdens [9]. However, recent research has led to a groundbreaking discovery: the prediction of HD can now be achieved through the use of artificial intelligence techniques [10].

References

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