Enhancing Stroke Prediction Dataset Performance Through Dataset Merging and Analyzing Important Feature

Abstract

Predictive modeling plays a vital role in stroke prediction, enabling timely intervention in healthcare. It is inevitable that any form of prediction especially in the prediction of stroke would require some form of predictive modeling that empowers timely healthcare intervention. This work can be used for proposing evaluation criteria of the stroke prediction model concerning the dataset size and feature influence. Two datasets were used: the first one with 5000 data points, the program had a 90% accuracy while the second with 1500 data points had 50% accuracy. Such transitions merged increased the datasets to a combined percentage of 60% to the other’s benefit, proving the importance of diverse datasets. Other measures pointing to the exploration of the feature importance analysis include Average Glucose Level and Body Mass Index (BMI) that are important in the accurate prediction of strokes. Therefore, these results present task, data set attributes, and the importance of features as crucial factors to be taken into account to build stable models. The nature of change in the difference in model accuracy between two datasets shows that large samples produce higher model accuracy. Future studies are likely to reveal improved models for different datasets and different subpopulations in order to ultimately enhance process accuracy and consequently stroke predictability as well as its effects on its patients. Understanding these factors can help enrich such determinants among the health-care providers which in turn would lead to improved ways of intervention and management of the risks associated with stroke. As such, this study emphasis the possibilities of predictive modeling to enhance the efficiency and effectiveness of the stroke prevention and care by providing accurate and reliable predictions.