Adopting random forest for predicting the risk of cerebrovascular disease and diabetes using appropriate database
1 College of Engineering, Northeastern University Boston, Massachusetts, USA.
2 Department of Information and Communication Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 2888-2899
Publication history:
Received on 16 November 2024; revised on 26 December 2024; accepted on 28 December 2024
Abstract:
Random forest is used to predict the possibility of the existence of a cerebrovascular disease subjects curated from the BraVa and diabetes datasets. Towards analyzing and prediction of cerebrovascular diseases, SPSS for realizing the independence and correlation between the various metrics that could contribute to a subject been diagnosed with a cerebrovascular disease. An analysis of the various metrics of the overall vascular size revealed a significant correlation especially between Total Length and Total Number of Branches (R = 0.829, p = 0.000). Metrics like Age, Contraction, Tortuosity, mean bifurcation Angle, mean bifurcation tilt which has implication of a cerebrovascular disease diagnosis according to study was used as the input for the random forest algorithm. The BraVa dataset which is the main datasets for this work was used to train the algorithm and a prediction of either “risky” or “Not risky” with a high accuracy of 100% was recorded. To further test the algorithm, a second datasets from the from the diabetes database which has a high number of subjects was also used to test the algorithm and a high accuracy of 90.256% was recorded. It was determined from the results that machine learning based Random Forest algorithm can be adopted as a prediction method especially on bigger dataset of neuromorphological measurements of neurons and it will aid or facilitate accurate prediction of any form of cerebrovascular disease and also aid in accurate medical diagnosis.
Keywords:
Random Forest; Cerebrovascular disease; Diabetes; Machine Learning; Brain Vasculature
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