Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm

Abstract

Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. However, these studies pay less attention to the predictors (both demographic and behavioural). Our study considers interpretability, robustness, and generalisation as key themes for deploying algorithms in the medical domain. Based on this background, we propose the use of random forest for stroke incidence prediction. Results from our experiment showed that random forest (RF) outperformed decision tree (DT) and logistic regression (LR) with a macro F1 score of 94%. Our findings indicated age and body mass index (BMI) as the most significant predictors of stroke disease incidence.

An Optimal House Price Prediction Algorithm: XGBoost

“…ML algorithms are of two main categories, namely the supervised and the unsupervised ML approach [14]. The supervised ML approach makes use of a subset of labeled data (where target variable is known) for training and testing on the remaining data to make predictions on unseen datasets [15]. Whilst the unsupervised ML approach does not require a labeled dataset, the approach facilitates the analysis (by uncovering hidden patterns) and makes prediction from unlabeled datasets [16].…”

“…It operates in a unique manner by giving less weight to weak features, resulting in faster processing compared with other methods. This characteristic makes it a reliable choice for handling missing or noisy data and outliers [14,15]. RF is versatile as it can tackle both classification and regression tasks.…”

“…The RF predictor consists of an M randomized regression tree. Considering the j th tree in a cluster of trees, the predicted value at every query point x is denoted by mn x; ∅j, ∂n , where ∅1 ............... , ∅m are the independent random variables and ∂n is the training variable [15]. The j th tree estimate is thus formulated as:…”

“…Random forest (RF)-introduced by [27]-is a robust and versatile ensemble learning technique that offers precise results for various types of datasets [14,15,28,29]. Widely recognized for its precision in predicting outcomes for various datasets, RF excels in handling high-dimensional data, capturing complex relationships, and mitigating overfitting.…”

Predictive modelling and identification of key risk factors for stroke using machine learning

“…These approaches and methods can improve patient outcomes and lower the societal and individual burden of stroke 25 . Addressing stroke prediction difficulties such as accuracy, missing data, data imbalance, and interpretability is critical to reaching the full potential of machine learning in this domain 26 .…”