Integrating Algorithms with Intuitive AI to Forecast the Likelihood of Cerebral Infarction in Patients Exhibiting Signs of Illness
Keywords:
Stroke, Ischemic stroke, Hemorrhagic stroke, Stroke classification, Artificial Intelligence algorithm , Normalized pointwise mutual information , Cerebrovascular disease. Random Forest , cluster grouping .Abstract
Stroke is a highly debilitating disease that is widespread globally. It is a major public health concern that requires urgent attention. Throughout their lifetimes, individuals and their families may experience the severe consequences of this complex and diverse neurological disorder. These consequences can be encountered by individuals. This case study examines the intricacies of stroke, encompassing its etiology, potential risks, manifestations, diagnosis, and therapeutic interventions using Intricate Artificial algorithm to forcast and predict the occurances of stoke using available patient symptoms. The system uses cluster grouping and random forest model to accurately predict the occurance of stroke based on lifestyle and symptoms of a group of patients classified based on gender It also encompasses concerns over the potential hazards linked to stroke. Moreover, the entire narrative underscores the importance of immediate action and comprehensive medical intervention. If there is a sudden interruption of blood flow to the brain, a stroke, often known as a "brain attack," will occur instantly. Consequently, the brain cells will be deprived of the necessary oxygen and nutrients required for optimal functioning. This interruption, which can be caused by clots (ischemic stroke) or ruptured blood vessels (hemorrhagic stroke), has the ability to cause damage to the neurological system and, in the most severe situation, permanent disability of the affected individual. Due to the significant impact of stroke on individuals' everyday functioning and quality of life, research on stroke is highly crucial.
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