Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke
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 adv...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123003710 |
| _version_ | 1797405475464019968 |
|---|---|
| author | Afnan Al-Ali Uvais Qidwai Saadat Kamran |
| author_facet | Afnan Al-Ali Uvais Qidwai Saadat Kamran |
| author_sort | Afnan Al-Ali |
| collection | DOAJ |
| description | 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. • Inputs: Real data about ischemic stroke represented by clinically relevant features. • Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan. • 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. |
| first_indexed | 2024-03-09T03:10:29Z |
| format | Article |
| id | doaj.art-f0fccc0906cb4f758d6b008d87419bc7 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:10:29Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-f0fccc0906cb4f758d6b008d87419bc72023-12-04T05:22:30ZengElsevierMethodsX2215-01612023-12-0111102375Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic strokeAfnan Al-Ali0Uvais Qidwai1Saadat Kamran2Computer Science and Engineering Department, Qatar University, Doha, Qatar; Corresponding author.Computer Science and Engineering Department, Qatar University, Doha, QatarDepartment of Neurology, Hamad Medical Corporation, Doha, QatarIschemic 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. • Inputs: Real data about ischemic stroke represented by clinically relevant features. • Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan. • 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.http://www.sciencedirect.com/science/article/pii/S2215016123003710Ischemic strokeAdaptive neuro-fuzzy inference system (ANFIS)Binary particle swarm optimization technique (BPSO)Infarction growth rate II (IGR II) |
| spellingShingle | Afnan Al-Ali Uvais Qidwai Saadat Kamran Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke MethodsX Ischemic stroke Adaptive neuro-fuzzy inference system (ANFIS) Binary particle swarm optimization technique (BPSO) Infarction growth rate II (IGR II) |
| 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 | Ischemic stroke Adaptive neuro-fuzzy inference system (ANFIS) Binary particle swarm optimization technique (BPSO) Infarction growth rate II (IGR II) |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123003710 |
| work_keys_str_mv | AT afnanalali predictinginfarctiongrowthrateiiusinganfisbasedbinaryparticleswarmoptimizationtechniqueinischemicstroke AT uvaisqidwai predictinginfarctiongrowthrateiiusinganfisbasedbinaryparticleswarmoptimizationtechniqueinischemicstroke AT saadatkamran predictinginfarctiongrowthrateiiusinganfisbasedbinaryparticleswarmoptimizationtechniqueinischemicstroke |