# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
###### Abstract  
Brain stroke is the leading cause of death and disability globally; hence, early identification and prediction are critical for better patient outcomes. Traditional diagnostic procedures, such as manually interpreting clinical images, are time consuming and error prone. This research investigates the use of hybrid deep learning models, such as recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs), to improve stroke prediction accuracy. The current study compared the performance of these individual models with the developed hybrid model on the brain stroke dataset. By merging these models, we reached an overall accuracy of 96% in identifying stroke risk as low, medium, or high. This categorization may offer healthcare practitioners actionable insights by assisting them and allowing them to make better decisions. This technique represents a substantial improvement in stroke prediction and preventive healthcare practices. The model's performance can further be tested with more complicated clinical and demographic data that will help to generalize the model for real-world clinical applications. Furthermore, combining this hybrid model with electronic health records (EHR) systems can also assist in early identification, tailored therapies, and improved stroke management, enhancing patient outcomes and lowering healthcare costs. p 2025 15 4639 +
Footnote †: journal: Applied sciences  
article  
[color=orange!20, color=none] [color=gray! weight on medical services frameworks is tremendous, featuring the significance of ideal determination to relieve the effect. Conventional strategies for diagnosing strokes, for example, clinical appraisals and neuroimaging methods like computed tomography (CT) and X-ray filters, have limits, including significant expenses, the requirement for specific skills, and postponents in analysis. These difficulties have driven the joining of computerized reasoning and artificial intelligence and machine learning (AIML) calculations into stroke finding and prediction [2]. Simulated intelligence, especially profound learning, has shown surprising potential in handling enormous datasets, recognizing designs, and giving precise stroke analysis. Convolutional neural networks (CNNs) have been especially successful in dissecting CT and X-ray pictures, essentially lessening finding times without compromising accuracy [2]. Given their significant effect on infection weight, handicap, and mortality, strokes are a significant worldwide general wellbeing concern. Artificial intelligence (AI) is changing stroke conclusions by empowering quicker, more exact recognizable proof through better examination of clinical pictures, recordings, and patient information, permitting medical services experts to make opportune and all-round informed decisions [3,4]. Moreover, man-made intelligence-controlled prescient examination is upsetting stroke care by surveying a singular's risk of stroke utilizing segment, clinical, and way of life information, working with early preventive actions [3]. Computer-based intelligence frameworks can convey customized risk appraisals by breaking down factors like age, pulse, glucose levels, smoking propensities, and clinical history [4]. This capacity is urgent for starting early treatment, consequently further developing patient endurance rates and personal satisfaction. Also, simulated intelligence empowered wearable gadgets that continuously screen essential signs and ready patients and medical services suppliers to potential stroke risk further improve forecast accuracy [4]. In instances of hemorrhagic strokes, which are brought about by breaks in mind veins and can be deadly if untreated, simulated intelligence-fueled apparatuses can rapidly separate ischemic and hemorrhagic strokes, empowering prompt treatment.