Sanjose Thomas
Sanjose A Thomas is a noted young professor, sociologist, author, public speaker, columnist, academic and environmentalist from Kerala. He has authored several articles in Sociology as well as in public policy & has an extensive research interest in the area of Sociology of development. He has won several International and National awards including ‘Vidya Saraswathi Puraskar’ for Best Research Scholar by Global Management Council and IJMTST Teaching Excellence Award. He is also the recipient of Sci- Tech International Award for best researcher instituted by International Scientific Research and Publications, USA. His research articles have appeared/been accepted in CARE/SCI/Scopus Indexed journals such as International Journal For Research Education and Scientific Methods and Economic and Political Weekly. He is the co-author of book Principles of Sociology, AGPH Books, ISBN -978-81-19025-79-4. Currently pursues Doctoral Studies under Dr. Sajitha J Kurup
Supervisors: Dr.Sajitha J Kurup Phd
Supervisors: Dr.Sajitha J Kurup Phd
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Papers by Sanjose Thomas
field of healthcare management through improving the
accuracy of diagnoses, effectiveness of treatments, and
overall experience of patients. This paper aims at
reviewing the utilization of four algorithms in the
healthcare sector namely; Decision Trees, Support
Vector Machines, Random Forests, and Neural
Networks. These algorithms are assessed on the basis of
performance indicators like accuracy, precision, recall
and F1 score using the patient record dataset of size
20,000 in the present work. Performance seems to show
that Neural Networks have the highest accuracy of 93
over the other algorithms. 5%, precision of 92. 0%, recall
of 93. 0 %, Precisions 92 %, Recal 93 % and F1 score of
92. 5%. This is followed by Random Forests with a 91.
3%, precision of 90. 0%, recall of 90. , accuracy of 5%,
and F1 score of 90. 2%. The research findings show that
SVM yields to 89 percent accuracy. 0%, precision of 87.
5%, recall of 88. 0%, Precision of 0.88 and F1 score of
0.874. for Decision Trees falls 85 % and for Naïve Bayes
it falls up to 73% whereas for Random Forest the
accuracy comes near to about 7%. 2%, precision of 82.
5%, recall of 84. The accuracy for the producers is 0%,
recall is 0%, and F1 score is 83. 2%. The comparative
analysis with the related works explores the pace and
progress of AI in various areas of the healthcare sector’s
concerns. Among the identified findings they include
proactive utilization of Artificial Intelligence in
enhancing the delivery of health care, use of Artificial
Intelligence in achieving patients’ better-quality health
care, and apply of Artificial Intelligence in the decision
making process. The future area is focused on the
improvement of the algorithm of AI, the ethical and legal
questions, and the usage of AI in the solving of the
worldwide health issues.
Keywords: Artificial Intelligence, Healthcare
Management, Neural Networks
applying enhanced forms of artificial intelligence in
improving management information system in
healthcare. By analyzing various methods of machine
learning, deep learning, NLP, and IoT analytics,
carrying out their analysis, as well as their practical
application, the given work sheds light on their
revolutionary role in health care. Some of the identified
works involved the use of Random Forests and
Convolutional Neural Networks Clinicians, achieving
prediction accuracy of 85 % and 92 % respectively in two
clinical decision support and Medical image analysis
tasks. In this study, to analyze entity recognition, the
NER process was supported by CRF and achieved an 88
% F1-score for identifying medical entities in textual
data, increasing the speed of clinical documentation.
Moreover, IoT based anomaly detection systems
obtained a 95% of the detection rate contributing to the
improvement of real time observation and urgent
response into the healthcare environments.
Keywords: Artificial Intelligence, Healthcare
Management