Artificial Neural Networks are biologically inspired computational methodologies that can perfom multifactorial analyses. In recent years, they have been evaluated for medical decision support, with varying degrees of success. The preliminary part of my thesis deals with evaluating whether an Artificial Neural Network can be trained to approximate a cardiovascular risk stratification algorithm by Rifai et al. My subsequent work involves training the network on a population-based cross-sectional dataset with the objective of categorizing Ankle-Brachial Index and Maximal Oxygen Consumption. These are indicators of the severity of lower extremity atherosclerosis and the level of cardiovascular fitness respectively.
NeuralSIM®, a commercially available Artificial Neural Network, was trained using C-reactive protein and Total Cholesterol/HDL Cholesterol ratio as input parameters, and the relative risk stratum for future myocardial infarctions or stroke as output. For the Ankle-Brachial Index and the cardiovascular fitness networks, data was obtained from the National Health and Nutrition Examination Survey. The network for cardiovascular fitness was compared with an algorithm published by Jackson et al.
The network was able to approximate the cardiovascular risk stratification algorithm by Rifai et al closely with correlation coefficients of0.95 in men and 0.93 in women respectively. The network to screen for low cardiovascular fitness had a sensitivity of 83% and a specificity of 78%, with an overall accuracy of 81%. The Ankle-Brachial Index network demonstrated a high level of specificity (86.3%) for estimating abnonnal values but a very low sensitivity (30%).
Artificial neural networks showed encouraging results for potential use as decision-support tools. One significant limitation is that the importance of individual parameters or the exact function cannot be ascertained easily. There is a need to address this issue in future software development.
","abstract_html":"<p>Artificial Neural Networks are biologically inspired computational methodologies that can perfom multifactorial analyses. In recent years, they have been evaluated for medical decision support, with varying degrees of success. The preliminary part of my thesis deals with evaluating whether an Artificial Neural Network can be trained to approximate a cardiovascular risk stratification algorithm by Rifai et al. My subsequent work involves training the network on a population-based cross-sectional dataset with the objective of categorizing Ankle-Brachial Index and Maximal Oxygen Consumption. These are indicators of the severity of lower extremity atherosclerosis and the level of cardiovascular fitness respectively.</p> <p>NeuralSIM®, a commercially available Artificial Neural Network, was trained using C-reactive protein and Total Cholesterol/HDL Cholesterol ratio as input parameters, and the relative risk stratum for future myocardial infarctions or stroke as output. For the Ankle-Brachial Index and the cardiovascular fitness networks, data was obtained from the National Health and Nutrition Examination Survey. The network for cardiovascular fitness was compared with an algorithm published by Jackson et al.</p> <p>The network was able to approximate the cardiovascular risk stratification algorithm by Rifai et al closely with correlation coefficients of0.95 in men and 0.93 in women respectively. The network to screen for low cardiovascular fitness had a sensitivity of 83% and a specificity of 78%, with an overall accuracy of 81%. The Ankle-Brachial Index network demonstrated a high level of specificity (86.3%) for estimating abnonnal values but a very low sensitivity (30%).</p> <p>Artificial neural networks showed encouraging results for potential use as decision-support tools. One significant limitation is that the importance of individual parameters or the exact function cannot be ascertained easily. There is a need to address this issue in future software development.</p>","abstract_has_math":false,"creators":["Patil, Ninad Mohan"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis - Pacific Access Restricted","degree_discipline":"Pharmaceutical and Chemical Sciences","degree_department":null,"school":null,"contributors":["Timothy J. Smith"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-01-01T08:00:00Z","date_published":"2005-01-01T08:00:00Z","updated_at":"2026-07-24T05:36:35Z","subjects":["Neural networks (Computer science)","Cardiovascular system","Blood volume","Medicinal and Pharmaceutical Chemistry","Medicine and Health Sciences","Pharmacy and Pharmaceutical Sciences"],"languages":[],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholarlycommons.pacific.edu/uop_etds/616","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Timothy J. Smith"]},{"key":"dc:creator","label":"Author","values":["Patil, Ninad Mohan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2005-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Pharmaceutical and Chemical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Pacific Access Restricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neural networks (Computer science)","Cardiovascular system","Blood volume","Medicinal and Pharmaceutical Chemistry","Medicine and Health Sciences","Pharmacy and Pharmaceutical Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarlycommons.pacific.edu/uop_etds/616"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Artificial Neural Networks are biologically inspired computational methodologies that can perfom multifactorial analyses. In recent years, they have been evaluated for medical decision support, with varying degrees of success. The preliminary part of my thesis deals with evaluating whether an Artificial Neural Network can be trained to approximate a cardiovascular risk stratification algorithm by Rifai et al. My subsequent work involves training the network on a population-based cross-sectional dataset with the objective of categorizing Ankle-Brachial Index and Maximal Oxygen Consumption. These are indicators of the severity of lower extremity atherosclerosis and the level of cardiovascular fitness respectively.
NeuralSIM®, a commercially available Artificial Neural Network, was trained using C-reactive protein and Total Cholesterol/HDL Cholesterol ratio as input parameters, and the relative risk stratum for future myocardial infarctions or stroke as output. For the Ankle-Brachial Index and the cardiovascular fitness networks, data was obtained from the National Health and Nutrition Examination Survey. The network for cardiovascular fitness was compared with an algorithm published by Jackson et al.
The network was able to approximate the cardiovascular risk stratification algorithm by Rifai et al closely with correlation coefficients of0.95 in men and 0.93 in women respectively. The network to screen for low cardiovascular fitness had a sensitivity of 83% and a specificity of 78%, with an overall accuracy of 81%. The Ankle-Brachial Index network demonstrated a high level of specificity (86.3%) for estimating abnonnal values but a very low sensitivity (30%).
Artificial neural networks showed encouraging results for potential use as decision-support tools. One significant limitation is that the importance of individual parameters or the exact function cannot be ascertained easily. There is a need to address this issue in future software development.
"]},{"key":"dc:source","label":"Dc Source","values":["50"]},{"key":"dc:title","label":"Title","values":["Artificial neural networks for cardiovascular risk, cardiovascular fitness and ankle-brachial index"]}]}],"canonical_facts":{"dc:contributor":["Timothy J. Smith"],"dc:creator":["Patil, Ninad Mohan"],"dc:date.available":["2005-01-01T08:00:00Z"],"dc:description.abstract":["Artificial Neural Networks are biologically inspired computational methodologies that can perfom multifactorial analyses. In recent years, they have been evaluated for medical decision support, with varying degrees of success. The preliminary part of my thesis deals with evaluating whether an Artificial Neural Network can be trained to approximate a cardiovascular risk stratification algorithm by Rifai et al. My subsequent work involves training the network on a population-based cross-sectional dataset with the objective of categorizing Ankle-Brachial Index and Maximal Oxygen Consumption. These are indicators of the severity of lower extremity atherosclerosis and the level of cardiovascular fitness respectively.
NeuralSIM®, a commercially available Artificial Neural Network, was trained using C-reactive protein and Total Cholesterol/HDL Cholesterol ratio as input parameters, and the relative risk stratum for future myocardial infarctions or stroke as output. For the Ankle-Brachial Index and the cardiovascular fitness networks, data was obtained from the National Health and Nutrition Examination Survey. The network for cardiovascular fitness was compared with an algorithm published by Jackson et al.
The network was able to approximate the cardiovascular risk stratification algorithm by Rifai et al closely with correlation coefficients of0.95 in men and 0.93 in women respectively. The network to screen for low cardiovascular fitness had a sensitivity of 83% and a specificity of 78%, with an overall accuracy of 81%. The Ankle-Brachial Index network demonstrated a high level of specificity (86.3%) for estimating abnonnal values but a very low sensitivity (30%).
Artificial neural networks showed encouraging results for potential use as decision-support tools. One significant limitation is that the importance of individual parameters or the exact function cannot be ascertained easily. There is a need to address this issue in future software development.
"],"dc:identifier":["https://scholarlycommons.pacific.edu/uop_etds/616"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:source":["50"],"dc:subject":["Neural networks (Computer science)","Cardiovascular system","Blood volume","Medicinal and Pharmaceutical Chemistry","Medicine and Health Sciences","Pharmacy and Pharmaceutical Sciences"],"dc:title":["Artificial neural networks for cardiovascular risk, cardiovascular fitness and ankle-brachial index"],"thesis:degree_discipline":["Pharmaceutical and Chemical Sciences"],"thesis:degree_level":["Thesis - Pacific Access Restricted"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T05:36:35Z"}