Machine Learning Algorithm for Stroke Disease Classification

Project Code :TCMAPY212

Objective

In this proposed model, we are using eight machine learning algorithms or stroke disease classification. Here, we preprocess the data to improve the image quality of CT scans of stroke patients by optimizing the quality of image to improve image results and to reduce noise, and also applying machine learning algorithms to classify the patients images into two sub-types of stroke disease.

Abstract

Machine learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health setting, offering personalized clinical care for stroke patients. ML applications in health care are growing, nonetheless there is a greater need for further investigation in some research fields.

Therefore, this study aimed to systematically review the state of the art on ML techniques for brain stroke and classify the research studies into 2 categories based on their functionalities. By using seven machine learning algorithms we can generate the predictions, they are K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Multi-layer Perceptron (MLP-NN) and Support Vector Machine.

Keywords: CT Scan Image, Machine Learning Algorithms, Stroke Ischemic, Stroke Hemorrhage.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.