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Top1. Introduction
In present time, stroke is the second leading disease responsible for untimely death of human being. In 2030, more than 12 million people could be died due to stroke disease and more than seventy million people could be stroke survivor (Feigin et al., 2014). A recent study showed that developed countries having high rate of stroke disease, but the middle and low income countries also having in the risk zone and cases of stroke diseases is rising rapidly in these countries (Kim et al., 2015). It is also seen that the one third stroke survival patients live with long term disability. Most of physicians describe the stroke as injury in brain and spinal code, in turn affects the blood supply. Stroke can be classified into three categories 1) Ischemic Stroke 2) Transient Ischemic Stroke 3) Haemorrhagic Stroke. Most common stroke is ischemic stroke and it is noticed that eighty seven percent strokes are ischemic. The reason behind this stroke is presence of clot or obstacle in the blood vessel of brain. The ischemic stroke having two types – embolic and thrombotic strokes (Pahus et al., 2016). The embolic strokes can be interpreted as the presence of clot in any part of the human body, but this clot blocks the flow of blood towards brain. Whereas, in case of thrombotic clot, the blood flow an artery is restrict due to a clot, in turn blood supply of brain affected. Haemorrhagic stroke is occurred due to burst of weak blood vessels. This stroke typically varies in between 10-15%, but it is more life threaten than ischemic stroke (Dupont et al., 2010; Santos et al., 2016). It is further classified into subarachnoid haemorrhage and intracerebral haemorrhage. Transient ischemic attack can be described as mini-stroke and occurs due to temporary blockage/clot. It causes temporary injury to brain tissues (Shinohara et al., 2011). But, it may be a warning message of additional stroke in future. Hence, it can be stated that stroke can considered as a fatal disease. It is observed that the treatment of stroke is risky and physicians can proceed with traditional treatment and examine whether the chance of risk is overcome or not. If, the diagnostic/monitoring tools are available for the treatment/ prediction of stroke patients, then, the current condition of stroke patient is evaluated using the current behaviour and also decided some initial treatment measures. Such tools can also predict the recovery rate of stroke patients. But, the tool with high accuracy rate can be very useful, in case of stroke treatment.
1.1 Motivation and Contribution of the Work
Several researchers addressed the prediction of stroke prognosis significantly and also suggested effective treatment and intervention (Khosla et al., 2010; Longstreth et al., 2001; Srivastava et al., 2020a; Srivastava et al., 2020b; Weng et al., 2017). Some studies also highlighted several features for the prediction of stroke disease such as creatinine level, time to walk, smoke etc., (Abdar et al., 2019). It is also observed that medical dataset consists of large number of features and it is very tough task to determine the potential features and verify the risk factor associated with these features manually. Nowadays, machine learning algorithm and meta-heuristic algorithms are widely adopted to determine the relevant features from medical datasets and made the features selection automatic rather than manual. It is observed that combination of feature selection and machine learning classifier improve the prediction accuracy in effective manner. Hence, this work also considers the feature selection technique for improving the prediction accuracy of machine learning classifier especially for stroke disease. The main contributions of this study are summarized as below:
- 1.
To design an optimized deep learning model for accurate prediction of stroke.
- 2.
To adopt an artificial bee colony based method for determining relevant features for stroke prediction.
- 3.
To examine the efficiency of ABC optimized deep learning technique on Stroke dataset.
- 4.
Simulation results proved that ABC optimized deep learning can be used as an earlier detection tool for stroke prediction.