# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 1 Introduction  
Stroke is a major cause of mortality and morbidity worldwide, and early identification of individuals at risk is crucial in reducing its clinical, economic, and societal impact [1, 2, 3]. In recent years, artificial intelligence (AI) has shown great potential in accurately predicting the risk of stroke. The Kaggle database, a popular platform for data science competitions, contains a comprehensive dataset that can be used to develop and validate AI models for stroke prediction. The Kaggle dataset [4], used here, includes demographic information, medical history, lifestyle factors, and physiological measurements from patients. These variables represent major determinants of stroke risk, such as age, hypertension, diabetes, smoking, and alcohol consumption. The dataset also includes information on comorbidities such as coronary artery disease, atrial fibrillation, and heart failure, which are known to increase the risk of stroke. The novelty of our paper is to apply AI-improved methodology to Stroke Prediction as a starting point. Machine learning algorithms can be trained on this data to predict the likelihood of an individual experiencing a stroke. This can help clinicians identify patients who are at high risk and implement preventative measures before a stroke occurs. Additionally, AI canhelp identify regularities in the data that may be missed by traditional statistical methods, leading to more accurate and personalized risk assessments. In this article, we explore the use of AI to predict stroke using the public dataset [4]. We analyze the various determinants included in the dataset and evaluate the performance of different machine learning models in predicting stroke risk. Our goal is to contribute to the growing body of research on the intersection of AI and public healthcare and to provide insights that can lead to improved stroke prevention and treatment strategies. There have been prediction models applied to different prediction tools for types of strokes [5; 6; 7]. In the paper, we apply classical estimation for the machine learning models. Comparisons to other estimation tools will be the subject of future research when we have access to more stroke datasets. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study's scope but also enhance the overall transparency and impact of our findings. Stroke occurs when the blood supply to a certain part of the brain is blocked or when a blood vessel bursts. The devastating brain attack means that parts of the brain could become permanently damaged or actually die after a stroke [8]. There are various risk factors for stroke, the major of which include: high blood pressure, diabetes, heart and blood vessel conditions, high cholesterol levels, smoking, age, sex, family history, genetics, and others [9]. According to the World Stroke Organization \"Stroke has already reached epidemic proportions. Globally 1 in 4 adults over the age of 25 will have a stroke in their lifetime\" [10]. This means that roughly 25% of humans over the age of 25 are at risk of experiencing a brain attack. According to the World Stroke Organization's \"Global Stroke Fact Sheet: 2022\" more than 12 million people had a stroke last year, over 62% of all strokes occur in people under 70 years of age [11]. This would imply that brain attacks are a serious problem in modern society. Diseases like brain attacks could be lethal and contemporary medicine must constantly look out for different ways to prevent strokes. One of the prevention methods, widely known amongst academics and displayed in this article, consists of implementing Machine learning models to classify stroke conditions in patients. Using Machine learning algorithms and training data models could drastically improve stroke prediction, and early classification could possibly result in saving lives.