Develop innovative methodology to optimally fill in missing values and predict progression on multiple sclerosis
type: CreativeWork
description: Applying Machine Learning (ML) to predict and track Multiple Sclerosis (MS) progression is a significant advancement in medical research, with the potential to enhance patient outcomes. Accurate MS prediction enables personalized treatment, timely interventions, and improved quality of life by slowing disease progression and preventing complications. This research aims to deepen our understanding of MS by developing ML models and comprehensive risk assessments to support early prognosis, guide treatment strategies, and reduce disease impact. A major challenge in medical research, especially in predicting MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), Random Forest (RF), and Multilayer Perceptron (MLP), are evaluated for imputation efficiency. RF and MLP perform best, achieving overall accuracies of 92% and 91.5%, respectively, in handling missing data more accurately than other models. Given the effectiveness of RF and MLP in capturing complex patterns in data, these models are selected for further development. The next step applies Multi-Imputation (MI) bootstrapping in a sequential manner, prioritizing features based on the strength of their relationships, as determined by Pearson correlation analysis. This statistical technique identifies features with the highest correlations, ensuring that attributes with stronger relationships with other attributes, are imputed first. These imputed features then inform the next imputation in the sequence, cooperating with the subsequent ranked feature in the order. Bootstrapping, a resampling technique that involves replacement, creates multiple training datasets by repeatedly sampling from the original data, enhancing the robustness of the imputation process. The proposed sequential imputat…
abstract: Applying Machine Learning (ML) to predict and track Multiple Sclerosis (MS) progression is a significant advancement in medical research, with the potential to enhance patient outcomes. Accurate MS prediction enables personalized treatment, timely interventions, and improved quality of life by slowing disease progression and preventing complications. This research aims to deepen our understanding of MS by developing ML models and comprehensive risk assessments to support early prognosis, guide treatment strategies, and reduce disease impact. A major challenge in medical research, especially in predicting MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), Random Forest (RF), and Multilayer Perceptron (MLP), are evaluated for imputation efficiency. RF and MLP perform best, achieving overall accuracies of 92% and 91.5%, respectively, in handling missing data more accurately than other models. Given the effectiveness of RF and MLP in capturing complex patterns in data, these models are selected for further development. The next step applies Multi-Imputation (MI) bootstrapping in a sequential manner, prioritizing features based on the strength of their relationships, as determined by Pearson correlation analysis. This statistical technique identifies features with the highest correlations, ensuring that attributes with stronger relationships with other attributes, are imputed first. These imputed features then inform the next imputation in the sequence, cooperating with the subsequent ranked feature in the order. Bootstrapping, a resampling technique that involves replacement, creates multiple training datasets by repeatedly sampling from the original data, enhancing the robustness of the imputation process. The proposed sequential imputat…
author: Pilehvari, Shima
language: en
date published: 2024-11
date modified: 2026-07-24T04:03:43Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/16780

PropertyValue
property ID: DOI
value: 10.82465/4679