Advancing Diagnosis Capabilities with Smart AI Techniques for Early Symptoms Prediction of Brain Stroke

Muhammad Aurangzeb Khan; Raza Ullah Khan; Rasool Jamal; Zahid Ullah; Amjad Khan; Farzeed Khan

Authors

Muhammad Aurangzeb Khan Department of Computer Science, University of Science and Technology Bannu, KP, Pakistan.
Raza Ullah Khan Department of Medicine, Lady Reading Hospital Peshawar, KP, Pakistan
Rasool Jamal Department of Medicine, Lady Reading Hospital Peshawar, KP, Pakistan
Zahid Ullah Department of Cardiology, Lady Reading Hospital Peshawar, KP, Pakistan
Amjad Khan Department of Computer Science, University of Science and Technology Bannu, KP, Pakistan
Farzeed Khan Department of Computer Science, University of Science and Technology Bannu, KP, Pakistan

Keywords:

Brain Stroke, Machine Learning, Deep Learning, smart diagnosis, Early Symptom Predictions

Abstract

The brain, a vital organ in the human body, can suffer severe damage during a Brain Stroke (BS) due to blocked blood vessels. The interruption in blood flow and nutrient supply leads to significant symptoms and is considered a medical emergency. BS often results in long-term neurological impairments, complications, or even death, underscoring its critical nature. The World Health Organization (WHO) estimates that BS is the most prevalent cause of disability and death globally. Failure to detect a stroke early may result in delayed treatment, leading to severe complications such as lifelong neurological impairment or death. Early identification with Machine Learning (ML) and Deep Learning (DL) approaches can improve the treatment of patients and reduce the long-term impacts of stroke. The purpose of this research is to predict the signs of a stroke taking place at an early stage employing ML and DL models. To evaluate the efficiency of the approach, a comprehensive training set for BS recognition was collected from a well-known source, Kaggle. The training dataset contains eleven attributes, including age, gender, hypertension, etc., with 5110 records. Multiple classification models, like Support Vector Machine (SVM), Gradient Boosting (XG Boost), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), K Nearest Neighbors (KNNs), and Artificial Neural Network (ANN), were efficiently employed in this study for the identification of initial signs of BS. The suggested ANN has a recognition accuracy of 94.35%, whereas RF has an identification rate of 94.15%. Both have about identical forecast accuracy for BS. The findings of the study revealed that ML and DL approaches have the potential to improve the identification of a variety of illnesses, such as BS, hence reducing the load and subjectivity issues in the medical field that existed owing to earlier traditional methods.

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