Predicting Alzheimer's Disease Progression Using Multimodal Longitudinal Analysis: A Machine Learning Approach / (Record no. 609555)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02219nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nadeem, Maryam |
| 245 ## - TITLE STATEMENT | |
| Title | Predicting Alzheimer's Disease Progression Using Multimodal Longitudinal Analysis: A Machine Learning Approach / |
| Statement of responsibility, etc. | Maryam Nadeem |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 67p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Patients with Mild Cognitive Impairment (MCI) face an increased risk of developing<br/>Alzheimer's disease (AD), highlighting the importance of early diagnosis for effective<br/>interventions and management of the disease. In our study, we investigated the<br/>progression of AD in patients initially diagnosed with MCI using multimodal<br/>longitudinal data. A classification based framework was proposed for MCI prediction<br/>with baseline data of 569 stable MCI (sMCI) and 268 progressive MCI (pMCI) patients.<br/>Employing three supervised machine learning (ML) algorithms—support vector machine<br/>(SVM), logistic regression (LR), Random Forest (RF) and incorporating features such as<br/>cognitive function assessments, MRI, PET scans, CSF biomarkers, and genetic APOE<br/>status, the classification accuracies of 83.4%, 80.2%, and 80% were achieved<br/>respectively. Significant differences were observed in the performance of the models,<br/>with the SVM notably outperforming both LR and RF (p < 0.05). Impaired memory<br/>function and lower clinical tests scores were found as primary indicators of MCI patients<br/>progressing towards AD. Although the fusion of all modalities yielded accurate results<br/>for predicting MCI progression to AD, our analysis revealed less significant differences<br/>in evaluation metrics when only cognitive test results were used as features. This suggests<br/>that cognitive assessments alone are nearly as effective in predicting MCI progression,<br/>which can lead to more cost-effective strategies in clinical settings. This study<br/>underscores the need for further research aimed at developing new tools to assist<br/>clinicians in prognostic decision making. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Ahmed Fuwad |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/43852">http://10.250.8.41:8080/xmlui/handle/123456789/43852</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 06/06/2024 | 610 | SMME-TH-1023 | Thesis |