Machine learning approaches for early detection and progression prediction of alzheimer’s disease (Record no. 23339)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250814163321.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250814b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 27084 |
| Author | Prasad, Preethika |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning approaches for early detection and progression prediction of alzheimer’s disease |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.10(3), Sep-Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Ghaziabad |
| Name of publisher, distributor, etc. | MAT Journals |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 53-67p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Alzheimer's disease (AD) is a degenerative neurological condition that declines cognitive ability; early detection is essential for successful treatment and care planning. The OASIS longitudinal dataset is used in this work to investigate machine learning algorithms for predicting early Alzheimer's disease and a collection of advanced machine learning models such as Random Forest, Gaussian Naive Bayes, and Support Vector Machine (SVM), SVM with kernel tricks, and Decision Tree. By focusing on structural MRI and demographic data, this research evaluates and compares these classifiers' predictive performance across several metrics, including precision, F1-score, and recall. In addition to overall classification accuracy, this work also provides an in-depth analysis of critical features like Mini-Mental State Examination (MMSE) scores, age, brain volume ratios (ASF, eTIV, and nWBV), and years of education for demented and non-demented categories. The results demonstrate that the Random Forest and SVM models show superior performance in terms of precision and recall. This research underscores the potential of using machine learning models as robust tools for early Alzheimer’s prediction and contributes valuable insights for clinical decision-making. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 27085 |
| Co-Author | Manjunath, T. N. |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Book Number | 2581-6969 |
| Title | Journal of computer science engineering and software testing |
| Place, publisher, and date of publication | Ghaziabad MAT Journals |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://matjournals.net/engineering/index.php/JOCSES/article/view/1099 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 14/08/2025 | 2025-1325 | 14/08/2025 | 14/08/2025 | Articles Abstract Database |