Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI)

Abstract

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.

Identification and Mitigation of Bias Using Explainable Artificial Intelligence (Xai) for Brain Stroke Prediction

“…This method could be extended and combined with other networks for the project Several papers have used Explainable AI in brain stroke prediction. The authors (Islam et al, 2022) The authors (Pamungkas et al, 2022) performed explainable AI using common features and discovered women had a higher risk of stroke compared to men, and those with late detection of heart diseases and hypertension still have a high risk of stroke.…”

“…With the aid of Artificial Intelligence (AI), we have seen unbelievable progressions in the medical profession (Pamungkas et al, 2022) (Chen et al, 2022;Kwon et al, 2019aKwon et al, , 2019bM et al, 2020)from cancer predictions using machine learning to covid-19 detection using deep learning. We may use data mining techniques to discover trends in the dataset now that there is a collection of medical records that have been labelled.…”

“…Numerous studies have looked at the significance of people's medical histories and lifestyle choices on the likelihood that they would have a stroke (Islam et al, 2022;Khosla et al, 2010;Pamungkas et al, 2022;View of Moving Toward Explainable Decisions of Artificial Intelligence Models for the Prediction of Functional Outcomes of Ischemic Stroke Patients, n.d.). The likelihood of stroke is currently predicted using machine learning models as well.…”