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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
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Dual-center RAPID-LSSVM: Radius-adaptive, probability and imbalance driven weighting for Alzheimer's diagnosis.

Mushir Akhtar1, A Quadir1, M Tanveer1

  • 1Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India.

Neural Networks : the Official Journal of the International Neural Network Society
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PubMed
Summary

We developed RAPID-LSSVM, a novel machine learning approach to improve Alzheimer's disease diagnosis. This method enhances accuracy by effectively handling noisy data and class imbalance in diagnostic datasets.

Keywords:
Alzheimer’s diseaseFuzzy theoryLeast squares support vector machineRAPID weighting schemeRobust classification

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a primary cause of dementia, with early diagnosis being a significant clinical challenge.
  • Existing machine learning models for AD detection often struggle with label noise, outliers, and imbalanced datasets, limiting their real-world applicability.
  • Robust and accurate diagnostic tools are crucial for timely intervention and management of Alzheimer's disease.

Purpose of the Study:

  • To introduce RAPID-LSSVM, a novel machine learning framework designed to improve the robustness and accuracy of early Alzheimer's disease detection.
  • To address common challenges in machine learning for medical diagnostics, including label noise, data outliers, and class imbalance.
  • To evaluate the performance of the proposed RAPID-LSSVM models against traditional methods using benchmark and real-world Alzheimer's datasets.

Main Methods:

  • Proposed RAPID (Radius-Adaptive, Probability and Imbalance Driven) weighting mechanism integrated with least-squares SVM (LSSVM).
  • Developed two RAPID-LSSVM models: RAPID-LSSVM-I (mean-center) and RAPID-LSSVM-II (median-center) for enhanced data handling.
  • RAPID incorporates radius-adaptive proximity weights, local class-probability adjustments, and an imbalance-ratio compensation term for improved classification.
  • Validated models on KEEL, UCI, and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets under clean and noisy conditions.

Main Results:

  • RAPID-LSSVM models demonstrated superior performance compared to baseline models across various datasets and noise conditions.
  • The radius-adaptive proximity weight effectively preserved boundary sample influence while enhancing robustness to central noise.
  • The dual-center approach (mean vs. median) provided flexibility in handling different data distributions and outlier presence.
  • Significant improvements in Alzheimer's disease diagnosis accuracy were observed using the RAPID-LSSVM models on the ADNI dataset.

Conclusions:

  • The proposed RAPID-LSSVM models offer a robust and effective solution for early Alzheimer's disease diagnosis, particularly in the presence of noisy or imbalanced data.
  • RAPID-LSSVM's flexible weighting mechanism significantly enhances classification performance and reliability in challenging datasets.
  • These findings highlight the potential of RAPID-LSSVM to advance the accuracy and reliability of machine learning applications in neurodegenerative disease diagnostics.