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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Elucidating Encephalitic Disintegration Morbidity Speculation Through Machine Learning |
| Authors Name: | Dharshini Vasagan T , Nithiyabharathi S |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_212285
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| Published Paper Id: | IJNRD2401108 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Abstract—The lifelong neurodevelopmental disorder known as Encephalitic Disintegration Morbidity Speculations (EDMS) usually shows up in early childhood and presents as behavioral, linguistic, and social difficulties. Advanced machine learning approaches are used in this study, and AdaBoost continuously performs better in improving the accuracy of EDMS prediction. The best prediction strategy that works for a wide range of age groups is created when Principal Component Analysis (PCA) and AdaBoost are combined. This research emphasizes the need for early EDMS detection by focusing on the crucial 2-4 month period after a kid is born. In early EDMS diagnosis, especially in children, the suggested ensemble-based model consistently performs better than baseline machine learning techniques, exhibiting superior diagnostic accuracy, precision, recall, and F1-Score. These results show promise for early interventions and improved outcomes for affected individuals and their families, and they represent a major advancement in the improvement of EDMS diagnostic tools. This research provides promise for reducing the long-term effects of this complex neurodevelopmental disorder and improving the quality of life for individuals with EDMS by supporting early detection and intervention techniques. |
| Keywords: | Encephalitic Disintegration Morbidity Speculations (EDMS), neurodevelopmental disorder, AdaBoost, data imbalance, Ensemble–based model, Principal Component Analysis (PCA). |
| Cite Article: | "Elucidating Encephalitic Disintegration Morbidity Speculation Through Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.b56-b62, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401108.pdf |
| Downloads: | 000118751 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401108 Registration ID: 212285 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: b56-b62 Country: Cuddalore, Tamil Nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401108 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401108 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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