Utilizing Artificial Intelligence For Prediction Of Functional Recovery Post Stroke

Over the previous 20 years, the mean stroke prevalence in various parts of India varied from 44.29 to 559/100,000 persons. These stroke  estimations were found to be higher than High Income Countries. In comparison to high income countries, early stroke mortality rates were also greater in India.

•Although the functional deficits after stroke may include cognitive, speech, visual, sensory and motor deficits, the most commonly recognized deficit after stroke is motor impairment that may have negative impact on the subject’s mobility and quality of life.

•By offering predictions, appropriate rehabilitation may be targeted, and patients may be discharged earlier.

•A precise estimation of a person’s likelihood of recovery would make it possible to set reasonable goals and direct the distribution of resources for rehabilitation.

•An effective tool for clinical research, development of healthcare economics policy, and support for clinical choices is a prediction model of functional recovery trajectory.

a)it might aid in early rehabilitation planning and long-term management.

b)it might give early signals or triggers for people who don’t heal as expected

c)survivors and caretakers could make necessary plans if they were aware of the potential timing of poor health consequences.

A comprehensive knowledge of the key variables is necessary for rehabilitation techniques that enhance post-stroke recovery results, these include: age, gender, comorbidities, stroke subtype, disability adjusted life years, etc.

•The benefit of machine learning approaches is their capacity to forecast outcomes on a single-subject stage while taking a wide range of factors into account. This capability is essential for their potential use in clinical procedures.

•Patterns and correlations that may not be obvious to human assessors can be found in the data analyzed by machine learning algorithms.

•There are many models to forecast functional outcomes after stroke, but it is challenging to use them in India due to cultural and regional variances.

•Therefore, the goal of this study is to identify the most reliable predictive model for functional recovery following stroke in the Indian population using machine learning algorithms.