An Artificial Intelligence Approach For Predicting Stroke Disease

Stroke is a worldwide health problem and one of the leading causes of disability. Stroke is the second major cause of death in the world. [1]. It is a medical condition due to insufficient supply of blood that is lack of oxygen and nutrients to the brain, which ruptured.

Blood flow may be interrupted either due to a clot in the blood vessel rupture. Stroke is mainly three type’s ischemic stroke, Hemorrhagic stroke, and transient ischemic accident. The ischemic stroke 85% occurred in the world. This type of stroke caused due to a clot in the blood vessel [2] and the second type of stroke that due to a rupture of the blood vessel is referred to as the Hemorrhagic stroke. TIA is a mini-stroke.

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Transient Ischemic Attack is changed from other stroke types. The blood flows to the brain, so it is blocked only for short time, not further than 10 minutes. This type of stroke finish up to must the main stroke within one year, they do not treatment, will must  important stroke in 3 months                               Artificial intelligence techniques are generally used in science and technology.AI is creation a computer and computer controlled-robot. They are many applications speech recognition, intelligent robot, and handwriting etc.

Artificial intelligence is mainly two type’s machine learning (ML) and natural language processing (NLP).Machine learning techniques are really valued exploring in forecasting the possibility of stroke. Machine learning is a technique of data analysis that logical model building. Risk factors are something that growths our chance of a getting disease. They mostly two types they are a modifiable risk factor and non-modifiable risk factor stroke.

The modifiable risk factors are age, gender, and family history. Age is getting older, so the risk of stroke is increased; gender other risk factors more common in men. Family history is depending history of heart attack and stroke [4]. Then important risk factor is hypertension. Then no modifiable risk factor is smoking, hypertension, high blood pressure, diabetes, physical inactivity, high blood cholesterol, alcohol. The blood pressure can cause injury to blood vessels, the main to stroke. Smoking increases bp and decreasing oxygen in the blood. Toxic chemicals have deposited the lungs; these chemicals harm the blood vessel.

This increase the accidental of clots the blood. Overweight is increased body fat they can contribute high blood pressure and high cholesterol. Alcohol is daily drinking can raise blood pressure to high levels. This case risk of both type’s hemorrhagic stroke and ischemic stroke.             Gil, Johnsson, Garicia, Paya,  [5]analyzed some ANN models as tools  maintenance in the identification of urological dysfunctions.

 This system using artificial neural networks (ANN). The mainly  two types of supervised and unsupervised neural network.Moein, Monadjemi, and Moallem [2] evaluated the procedure of diagnosis which usually is employed by doctors and changed to  machine-implementable format. Then after choosing some symptoms of eight altered diseases, a data set consist the data of a hundred cases were arranged and applied to a machine learning neural network. The results are the advantages of using a fuzzy approach.

The main aim is to suggest the role of effective symptoms collection and the advantages of data fuzzification on a neural network-based automatic diagnosis classification.


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