학술논문

Impact of modifiable and non-modifiable risk factors on the prediction of stroke disease
Document Type
Conference
Source
2017 International Conference on Trends in Electronics and Informatics (ICEI) Trends in Electronics and Informatics (ICEI), 2017 International Conference on. :985-989 May, 2017
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Hypertension
Diabetes
Vegetation
Support vector machines
Training
Kernel
Market research
Stroke
modifiable and non-modifiable risk factors
machine learning
prediction
Language
Abstract
The entire paper portrays the way in which the occurrence of stroke is predicted by modifiable and non-modifiable factors such as diabetes mellitus (DM), hypertension, gender, age of the patients. The proposed prototype describes how these factors have a great impact on the prediction of stroke. The working protocol acquires details of patients through their case sheets from Sugam Multispeciality Hospital, as a part of data collection and it is preprocessed to overcome redundancy. As a next step the processed data is fed into various machine learning algorithms to fetch the evaluation results of various risk factors. The outcome of the entire working protocol determines that more than 50% of the patients are affected by stroke because of its incidence with risk factors such as DM, hypertension, above 40 years of age and its predominant more towards the male gender than female.