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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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Foresighting Outcomes and Risk Evaluation With Computational Artificial Intelligence in Stroke Trials: The FORECAST

Dipannita Adhikary¹, Adneen Moureen², Gie Ken-Dror¹

¹Biological Sciences, Royal Holloway, University of London, London, GBR.

|December 15, 2025

Summary

This study develops an AI model for stroke risk in Bangladesh, incorporating unique local factors like mental health. The FORECAST study aims for over 90% accuracy in predicting stroke occurrence and outcomes.

Keywords:

risk prediction model stroke stroke condition in bangladesh stroke prognosis stroke risk predictors

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Area of Science:

Neurology
Artificial Intelligence
Public Health

Background:

Stroke is a major cause of death and disability in Bangladesh.
Existing risk models do not account for local socioeconomic, environmental, and mental health factors.
There is a need for a tailored stroke prediction model for the Bangladeshi population.

Purpose of the Study:

To develop an artificial intelligence (AI)-based risk prediction model for stroke occurrence and poor long-term outcomes in Bangladesh.
To incorporate unique local risk factors, including rural-urban disparities and mental health comorbidities.
To establish the first AI-based stroke prediction model for Bangladesh.

Main Methods:

An ambispective case-control study (FORECAST) enrolling 4,000 participants (2,000 cases, 2,000 controls).
Development of ensemble machine learning models using comprehensive phenotypic data.
Application of explainable AI techniques and k-fold cross-validation for interpretability and validation.

Main Results:

The study protocol outlines the development of an AI-based stroke prediction model.
Expected accuracy exceeding 90% based on recent advances.
The model will account for unique Bangladeshi risk factors.

Conclusions:

The FORECAST study will provide the first AI-driven stroke prediction model for Bangladesh.
This model will address critical gaps in stroke risk assessment in low-middle-income countries.
Incorporating culturally relevant factors into AI frameworks enhances stroke prediction accuracy.