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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Pritam Chakraborty¹, Anjan Bandyopadhyay¹, Preeti Padma Sahu¹

¹School of computer engineering, KIIT University, Patia, Bhubaneswar, Odisha, 751024, India.

BMC Bioinformatics

|October 15, 2024

Summary

This study developed a machine learning model for stroke prediction, achieving 98.6% accuracy. The approach effectively uses principal component analysis (PCA) and ensemble methods for better healthcare outcomes.

Keywords:

Class imbalance Early intervention Feature selection Healthcare analytics Machine learning Predictive modeling Principal component analysis (PCA)Stacking ensemble Stroke prediction

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Healthcare Informatics
Biomedical Data Science
Computational Medicine

Background:

Stroke prediction is vital for timely intervention and improved patient outcomes.
Existing methods face challenges in accurately identifying individuals at risk.
Machine learning offers potential for enhanced predictive capabilities.

Purpose of the Study:

To evaluate machine learning techniques for stroke prediction.
To optimize principal component analysis (PCA) and stacking ensemble models.
To identify key demographic, clinical, and lifestyle predictors of stroke.

Main Methods:

Utilized principal component analysis (PCA) with systematic variation of components.
Implemented a stacking ensemble model including random forest, decision tree, and K-nearest neighbors (KNN).
Compared performance against traditional algorithms like SVM, logistic regression, and Naive Bayes.

Main Results:

Optimized PCA to 16 components for maximum predictive accuracy.
Achieved a stroke prediction accuracy of 98.6%.
Demonstrated superior performance over traditional machine learning algorithms, handling class imbalance effectively.

Conclusions:

The proposed PCA and stacking ensemble method significantly enhances stroke prediction accuracy.
This advanced machine learning approach offers a robust tool for early stroke risk identification.
Findings support the integration of sophisticated AI models in clinical decision-making for stroke prevention.