Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Md. Asadullah
In recent years Machine learning has been used for disease diagnosis and prediction in the public healthcare sector. It plays an essential role in healthcare and is rapidly being applied to education. It is one of the driving forces in science and technology, but the emergence of big data involves paradigm shifts in the implementation of machine learning techniques from traditional methods. Computers are now well equipped to diagnose many health issues with large health care datasets and progressions in machine learning techniques. Researchers have been used several machine learning techniques in public health. Several methods, including Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (KNN), are widely used in predictive model design research, resulting in effective and accurate decision-making. The predictive models discussed here are based on different supervised ML techniques and various input characteristics and data samples. Therefore, the predictive models can be used to support healthcare professionals and patients globally to improve public health as well as global health. Finally, we provide some problems and challenges which face the researcher in public health.
Md. Asadullah. 2021. \u201cMachine Learning in Public Health: A Review\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 21 (GJRE Volume 21 Issue F3): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
Total Score: 105
Country: Bangladesh
Subject: Global Journal of Research in Engineering - F: Electrical & Electronic
Authors: Md. Asadullah, Mamunar Rashid, Priyanka Bosu, Emon Ahmed, Sabeha Tamanna (PhD/Dr. count: 0)
View Count (all-time): 230
Total Views (Real + Logic): 2056
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Publish Date: 2021 08, Wed
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In recent years Machine learning has been used for disease diagnosis and prediction in the public healthcare sector. It plays an essential role in healthcare and is rapidly being applied to education. It is one of the driving forces in science and technology, but the emergence of big data involves paradigm shifts in the implementation of machine learning techniques from traditional methods. Computers are now well equipped to diagnose many health issues with large health care datasets and progressions in machine learning techniques. Researchers have been used several machine learning techniques in public health. Several methods, including Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (KNN), are widely used in predictive model design research, resulting in effective and accurate decision-making. The predictive models discussed here are based on different supervised ML techniques and various input characteristics and data samples. Therefore, the predictive models can be used to support healthcare professionals and patients globally to improve public health as well as global health. Finally, we provide some problems and challenges which face the researcher in public health.
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