Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke
<p>Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. R...
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2023
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| _version_ | 1864513528801525760 |
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| author | Afnan Al-Ali (16888695) |
| author2 | Uvais Qidwai (16888698) Saadat Kamran (4919416) |
| author2_role | author author |
| author_facet | Afnan Al-Ali (16888695) Uvais Qidwai (16888698) Saadat Kamran (4919416) |
| author_role | author |
| dc.creator.none.fl_str_mv | Afnan Al-Ali (16888695) Uvais Qidwai (16888698) Saadat Kamran (4919416) |
| dc.date.none.fl_str_mv | 2023-09-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.mex.2023.102375 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Predicting_infarction_growth_rate_II_using_ANFIS-based_binary_particle_swarm_optimization_technique_in_ischemic_stroke/25107998 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Biochemistry and cell biology Ischemic stroke Adaptive neuro-fuzzy inference system (ANFIS) Binary particle swarm optimization technique (BPSO) Infarction growth rate II (IGR II) |
| dc.title.none.fl_str_mv | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. Recent advancements in artificial intelligence (AI) have improved this prediction, offering crucial insights into the progression dynamics of ischemic stroke. One such promising technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), has shown potential, but it faces the 'curse of dimensionality' and long training times as the number of features increased. This paper introduces an innovative, automatic method that combines Binary Particle Swarm Optimization (BPSO) with ANFIS architecture, achieves reduction in dimensionality by reducing the number of rules and training time. By analyzing the Pearson correlation coefficients and P-values, we selected clinically relevant features strongly correlated with the Infarction Growth Rate (IGR II), extracted after one CT scan. We compared our model's performance with conventional ANFIS and other machine learning techniques, including Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression. </p> <p>• Inputs: Real data about ischemic stroke represented by clinically relevant features. </p> <p>• Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan. </p> <p>• Results: The model achieved commendable statistical metrics, which include a Root Mean Square Error of 0.091, a Mean Squared Error of 0.0086, a Mean Absolute Error of 0.064, and a Cosine distance of 0.074.</p> <h2>Other Information</h2> <p>Published in: MethodsX<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.mex.2023.102375" target="_blank">https://dx.doi.org/10.1016/j.mex.2023.102375</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0214338367aabae5974a7eea6f8bdbec |
| identifier_str_mv | 10.1016/j.mex.2023.102375 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25107998 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic strokeAfnan Al-Ali (16888695)Uvais Qidwai (16888698)Saadat Kamran (4919416)Biological sciencesBiochemistry and cell biologyIschemic strokeAdaptive neuro-fuzzy inference system (ANFIS)Binary particle swarm optimization technique (BPSO)Infarction growth rate II (IGR II)<p>Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. Recent advancements in artificial intelligence (AI) have improved this prediction, offering crucial insights into the progression dynamics of ischemic stroke. One such promising technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), has shown potential, but it faces the 'curse of dimensionality' and long training times as the number of features increased. This paper introduces an innovative, automatic method that combines Binary Particle Swarm Optimization (BPSO) with ANFIS architecture, achieves reduction in dimensionality by reducing the number of rules and training time. By analyzing the Pearson correlation coefficients and P-values, we selected clinically relevant features strongly correlated with the Infarction Growth Rate (IGR II), extracted after one CT scan. We compared our model's performance with conventional ANFIS and other machine learning techniques, including Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression. </p> <p>• Inputs: Real data about ischemic stroke represented by clinically relevant features. </p> <p>• Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan. </p> <p>• Results: The model achieved commendable statistical metrics, which include a Root Mean Square Error of 0.091, a Mean Squared Error of 0.0086, a Mean Absolute Error of 0.064, and a Cosine distance of 0.074.</p> <h2>Other Information</h2> <p>Published in: MethodsX<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.mex.2023.102375" target="_blank">https://dx.doi.org/10.1016/j.mex.2023.102375</a></p>2023-09-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.mex.2023.102375https://figshare.com/articles/journal_contribution/Predicting_infarction_growth_rate_II_using_ANFIS-based_binary_particle_swarm_optimization_technique_in_ischemic_stroke/25107998CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/251079982023-09-17T03:00:00Z |
| spellingShingle | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke Afnan Al-Ali (16888695) Biological sciences Biochemistry and cell biology Ischemic stroke Adaptive neuro-fuzzy inference system (ANFIS) Binary particle swarm optimization technique (BPSO) Infarction growth rate II (IGR II) |
| status_str | publishedVersion |
| title | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| title_full | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| title_fullStr | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| title_full_unstemmed | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| title_short | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| title_sort | Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke |
| topic | Biological sciences Biochemistry and cell biology Ischemic stroke Adaptive neuro-fuzzy inference system (ANFIS) Binary particle swarm optimization technique (BPSO) Infarction growth rate II (IGR II) |