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      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <Volume-Issue>volume12 issue 3</Volume-Issue>
      <Season>March 2023</Season>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>Pattern Recognition of Cardiac Arrest In Neonates Using Machine Learning Techniques</ArticleTitle>
      <Abstract>The period from which a baby is born estimated as four weeks after the last day of birth has a chance to suffer cardiac arrest. Cardiac arrest in the newborn is often heard in children of childbearing age. This news is encouraging to young mothers, causing them anxiety and sleeplessness. Since the origin of the sadness must be clarified, there is certainly reason for observation, because it can indicate a serious illness. This is the case when awareness is not required. Regardless of everything, after a careful examination, it is much nicer than not to have time and opportunity to restore the health of the child. In this paper, Pattern Recognition has proposed of Cardiac Arrest in Neonates Using Machine Learning Techniques. Foreign sounds heard in the pause between tones are called cardiac arrests; they do not take into account the characteristics of the normal activity of the heart, drowning out its tones. Anatomic anomalies of cardiac origin make up a third of all, prompting an increase in the frequency of this pathology. Approximately 0.7-1.2% of children are born with heart defects, and most of them die by the end of the first year of life without surgical correction. The probability of having a child with structural abnormalities of the heart and blood vessels in the family, where there is already a child with this disease, is slightly higher - about 5%.</Abstract>
      <Keywords>Cardiac Arrest; Newborn; Children; Serious Illness; Pattern Recognition; Anatomic anomalies;</Keywords>
        <Abstract>https://ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=14349&amp;title=Pattern Recognition of Cardiac Arrest In Neonates Using Machine Learning Techniques</Abstract>