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Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari

KNIT Sultanpur, Sultanpur, 228118, India

* Corresponding Author: Anuj Singh. Email: email

Computer Systems Science and Engineering 2023, 45(1), 513-530.

A correction of this article was approved in:

Correction: Prediction of Alzheimer’s Using Random Forest with Radiomic Features
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Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.


Cite This Article

APA Style
Singh, A., Kumar, R., Tiwari, A.K. (2023). Prediction of alzheimer’s using random forest with radiomic features. Computer Systems Science and Engineering, 45(1), 513-530.
Vancouver Style
Singh A, Kumar R, Tiwari AK. Prediction of alzheimer’s using random forest with radiomic features. Comput Syst Sci Eng. 2023;45(1):513-530
IEEE Style
A. Singh, R. Kumar, and A.K. Tiwari "Prediction of Alzheimer’s Using Random Forest with Radiomic Features," Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 513-530. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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