A CNN-GRU framework for stroke-heart attack prediction using IMOWPA-tuned SMOTE and LZMA compression.

The disparity in the data from intensive care units, where stroke victims and heart attack patients make up a minority, makes this effort extremely difficult. A well-known difficulty in data mining is handling unbalanced data. The main contribution of this work is a method that accurately identifies and categorises minority-class data, even in highly imbalanced datasets with small class sizes. This work predicts stroke from the balanced and compressed data from MIMIC III dataset. The Convolutional Neural Network-Gated Recurrent Unit with Imbalanced Data Handling (CNN-GRU-IDH) is proposed. Additionally, it reduces the amount of data transferred by compressing healthcare data using the Lempel Ziv Markov Chain Algorithm (LZMA). Class imbalance problems are addressed with the Synthetic Minority Over-sampling Technique (SMOTE). Notably, this study adds a novel element by employing the Improved Multi-Objective Wolf Pack Algorithm (IMOWPA) to choose the appropriate K nearest neighbour value for SMOTE. The suggested model surpasses existing models when used on the dataset, obtaining a remarkable accuracy rate of 87.66% and 85.63% of F1 score for 70% of training and 30% of testing data. The CNN-GRU-IDH approach, which tries to forecast the incidence of strokes, is used as the major data classification technique. This study makes a substantial advancement to improving patient-specific early stroke prediction, which might save lives and lower death rates.