Enhanced Forecasting of Alzheimer’s Disease Progression Using Higher-Order Circular Pythagorean Fuzzy Time Series
Journal Title: Healthcraft Frontiers - Year 2023, Vol 1, Issue 1
Abstract
This study introduces an advanced forecasting method, utilizing a higher-order circular Pythagorean fuzzy time series (C-PyFTSs) approach, for the prediction of Alzheimer’s disease progression. Distinct from traditional forecasting methodologies, this novel approach is grounded in the principles of circular Pythagorean fuzzy set (C-PyFS) theory. It uniquely incorporates both positive and negative membership values, further augmented by a circular radius. This design is specifically tailored to address the inherent uncertainties and imprecisions prevalent in medical data. A key innovation of this method is its consideration of the circular nature of time series, which significantly enhances the accuracy and robustness of the forecasts. The higher-order aspect of this forecasting method facilitates a more comprehensive predictive model, surpassing the capabilities of existing techniques. The efficacy of this method has been rigorously evaluated through extensive experiments, benchmarked against conventional time series forecasting methods. The empirical results underscore the superiority of the proposed method in accurately predicting the trajectory of Alzheimer’s disease. This advancement holds substantial promise for improving prognostic assessments in clinical settings, offering a more nuanced understanding of disease progression.
Authors and Affiliations
Muhammad Shakir Chohan, Shahzaib Ashraf, Keles Dong
Keywords
Related Articles
- EP ID EP732242
- DOI https://doi.org/10.56578/hf010104
- Views 36
- Downloads 0
Segmentation and Classification of Skin Cancer in Dermoscopy Images Using SAM-Based Deep Belief Networks
In the field of computer-aided diagnostics, the segmentation and classification of biomedical images play a pivotal role. This study introduces a novel approach employing a Self-Augmented Multistage Deep Learning Network...
Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality
The depth of a decision tree (DT) affects the performance of a DT classifier in predicting mortality caused by heart failure (HF). A deeper tree learns complex patterns within the data, theoretically leading to better pr...
Pneumonia Detection Technique Empowered with Transfer Learning Approach
Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep l...
Investigating Malaria Susceptibility in Central Maluku District: A Focus on Anopheles Mosquito Habitats
Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Rege...
Enhanced Forecasting of Alzheimer’s Disease Progression Using Higher-Order Circular Pythagorean Fuzzy Time Series
This study introduces an advanced forecasting method, utilizing a higher-order circular Pythagorean fuzzy time series (C-PyFTSs) approach, for the prediction of Alzheimer’s disease progression. Distinct from traditional...