  The negative impact of stroke in society has led to concerted efforts to
improve the management and diagnosis of stroke. With an increased synergy
between technology and medical diagnosis, caregivers create opportunities for
better patient management by systematically mining and archiving the patients'
medical records. Therefore, it is vital to study the interdependency of these
risk factors in patients' health records and understand their relative
contribution to stroke prediction. This paper systematically analyzes the
various factors in electronic health records for effective stroke prediction.
Using various statistical techniques and principal component analysis, we
identify the most important factors for stroke prediction. We conclude that
age, heart disease, average glucose level, and hypertension are the most
important factors for detecting stroke in patients. Furthermore, a perceptron
neural network using these four attributes provides the highest accuracy rate
and lowest miss rate compared to using all available input features and other
benchmarking algorithms. As the dataset is highly imbalanced concerning the
occurrence of stroke, we report our results on a balanced dataset created via
sub-sampling techniques.
