Authors:
Masaaki Suzuki
1
;
Toshiyuki Haruhara
1
;
Hiroyuki Takao
2
;
Takashi Suzuki
3
;
Soichiro Fujimura
4
;
Toshihiro Ishibashi
5
;
Makoto Yamamoto
6
;
Yuichi Murayama
5
and
Hayato Ohwada
1
Affiliations:
1
Department of Industrial Administration, Tokyo University of Science, Chiba and Japan
;
2
Department of Neurosurgery, Jikei University School of Medicine, Tokyo, Japan, Department of Innovation for Medical Information Technology, Jikei University School of Medicine, Tokyo, Japan, Department of Mechanical Engineering, Tokyo University of Science, Tokyo and Japan
;
3
Department of Innovation for Medical Information Technology, Jikei University School of Medicine, Tokyo and Japan
;
4
Department of Innovation for Medical Information Technology, Jikei University School of Medicine, Tokyo, Japan, Department of Mechanical Engineering, Tokyo University of Science, Tokyo and Japan
;
5
Department of Neurosurgery, Jikei University School of Medicine, Tokyo and Japan
;
6
Department of Mechanical Engineering, Tokyo University of Science, Tokyo and Japan
Keyword(s):
Artificial Intelligence, Machine Learning, Medical Data, Simulation Data, Computational Fluid Dynamics, Stroke, Subarachnoid Hemorrhage, Cerebral Aneurysm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Stroke is a serious cerebrovascular condition, in which brain cells die due to an abrupt blockage of arteries supplying blood and oxygen or due to bleeding in the brain tissue when a blood vessel bursts or ruptures. Because stroke occurs suddenly in most people, prevention is oftentimes difficult. In Japan, this condition is one of the major causes of death, which is associated with high medical cost, especially among the society’s aging population. Therefore, stroke prediction and treatment is important. Stroke incidences can be avoided by a preventive treatment based on the risk of onset. However, since judgment of the onset risk largely depends on the individual experience and skill of the doctor, a highly accurate prediction method that is independent of the doctor’s experience and skill is the focus of this study. The target of prediction for this research is subarachnoid hemorrhage that is part of stroke. Logistic regression and support vector machine that predict cerebral aneu
rysm rupture by machine learning using combined medical data and cerebral blood-flow-simulation data were employed to analyze 338 cerebral aneurysm samples (35 ruptured, 303 unruptured). SMOTE algorithm solved the imbalance of data, while the SelectKBest algorithm was used to extract important features from the total 70 features obtained from both data. Out of the 27 important features extracted, 40% belonged to the medical data and the remaining 60% were from the blood-flow-simulation data. Using logistic regression as a classification model, we found the sensitivity of 0.64 and the specificity of 0.85. The results validated the possibility of a highly accurate method of cerebral aneurysm rupture prediction by machine learning using engineering information obtained from mechanical simulation.
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