Early and accurate detection and diagnosis of heart disease using intelligent computational model

Autor: Kil To Chong, Yar Muhammad, Muhammad Tahir, Maqsood Hayat
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
Heart disease
Computer science
lcsh:Medicine
Diseases
02 engineering and technology
Disease
030204 cardiovascular system & hematology
computer.software_genre
Machine Learning
0302 clinical medicine
Human disease
0202 electrical engineering
electronic engineering
information engineering
lcsh:Science
Aged
80 and over
Multidisciplinary
medicine.diagnostic_test
Follow up studies
Middle Aged
Prognosis
Cardiovascular diseases
Female
020201 artificial intelligence & image processing
Adult
Heart Diseases
Feature vector
Heart failure
Feature selection
Machine learning
Article
03 medical and health sciences
Artificial Intelligence
medicine
Humans
Aged
Models
Statistical
business.industry
lcsh:R
Health care
Computational Biology
medicine.disease
Computational biology and bioinformatics
Statistical classification
ROC Curve
Angiography
lcsh:Q
Artificial intelligence
business
Classifier (UML)
computer
Follow-Up Studies
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-17 (2020)
Scientific Reports
ISSN: 2045-2322
Popis: Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.
Databáze: OpenAIRE
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