Harnessing Artificial Intelligence in Early Detection and Diagnosis of Alzheimer's Disease: Current and Future Applications DOI

Alaa Abdelfattah,

Waseem Sajjad, Imtiaz Ali Soomro

et al.

Indus journal of bioscience research., Journal Year: 2025, Volume and Issue: 3(2), P. 199 - 212

Published: Feb. 25, 2025

Alzheimer's Disease (AD) is a neurodegenerative disorder requiring early detection. This study compares AI models—Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF)—in analyzing neuroimaging data (MRI, PET) to enhance AD prediction improve diagnosis using machine learning techniques. Through the application of multi-modal in form genetic, clinical, data, also investigates effectiveness combining different types predictability models for diagnosis. Feature importance analysis was performed methods like SHAP (SHAP (Shapley Additive Explanations) values determine most important variables model predictions, e.g., certain brain regions or genetic components. The generalizability real-world applicability by training on an independent dataset representing diverse clinical settings. performance each assessed variety statistical measures accuracy, precision, recall, F1-score, Area Under Curve (AUC). findings showed that CNN better compared SVM RF all metrics with highest accuracy (92%), precision (93%), recall (91%), AUC (0.95). suggest effectively detects subtle patterns, making it strong tool While well, superior accuracy. Cross-validation confirmed its generalizability, crucial use. Implementing models, especially CNN, may enable earlier detection, timely interventions, improved patient outcomes Alzheimer’s care. References

Language: Английский

Harnessing Artificial Intelligence in Early Detection and Diagnosis of Alzheimer's Disease: Current and Future Applications DOI

Alaa Abdelfattah,

Waseem Sajjad, Imtiaz Ali Soomro

et al.

Indus journal of bioscience research., Journal Year: 2025, Volume and Issue: 3(2), P. 199 - 212

Published: Feb. 25, 2025

Alzheimer's Disease (AD) is a neurodegenerative disorder requiring early detection. This study compares AI models—Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF)—in analyzing neuroimaging data (MRI, PET) to enhance AD prediction improve diagnosis using machine learning techniques. Through the application of multi-modal in form genetic, clinical, data, also investigates effectiveness combining different types predictability models for diagnosis. Feature importance analysis was performed methods like SHAP (SHAP (Shapley Additive Explanations) values determine most important variables model predictions, e.g., certain brain regions or genetic components. The generalizability real-world applicability by training on an independent dataset representing diverse clinical settings. performance each assessed variety statistical measures accuracy, precision, recall, F1-score, Area Under Curve (AUC). findings showed that CNN better compared SVM RF all metrics with highest accuracy (92%), precision (93%), recall (91%), AUC (0.95). suggest effectively detects subtle patterns, making it strong tool While well, superior accuracy. Cross-validation confirmed its generalizability, crucial use. Implementing models, especially CNN, may enable earlier detection, timely interventions, improved patient outcomes Alzheimer’s care. References

Language: Английский

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