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  4. Vol 7, Issue 9, 2023

Prediction of Alzheimer's Disease Based on Random Forest Model

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DOI: 10.23977/acss.2023.070912 | Downloads: 8 | Views: 237

Author(s)

Anran Lei 1, Jin Wang 2, Shicheng Zhou 2

Affiliation(s)

1 School of Electrical Engineering and Automation, Hubei Normal University, Huangshi, 435002, China
2 School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China

Corresponding Author

Anran Lei

ABSTRACT

Alzheimer's disease is a syndrome characterized by acquired cognitive impairment, leading to significant declines in daily life, learning, work, and social functioning. It has a profound impact on the lives of elderly people, making early detection and treatment of Alzheimer's disease an urgent issue. This paper collects relevant data from patients with Alzheimer's disease in a certain hospital, explores the data using histograms, density probability graphs, box plots, and correlation coefficient heat maps after preprocessing. Then it compares the performance of logistic regression classification models, random forest classification models, and REF-random forest models in predicting the accuracy of Alzheimer's disease categories. The results show that the REF-random forest model achieves the highest prediction accuracy. Finally, this paper uses the SMOTE algorithm to process the data and further improve the accuracy of the model. The optimized REF-random forest model has achieved outstanding results in all indicators.

KEYWORDS

Alzheimer's Disease, Logistic Regression Model, Random Forest Model, REF, SMOTE Algorithm

CITE THIS PAPER

Anran Lei, Jin Wang, Shicheng Zhou, Prediction of Alzheimer's Disease Based on Random Forest Model. Advances in Computer, Signals and Systems (2023) Vol. 7: 87-94. DOI: http://dx.doi.org/10.23977/acss.2023.070912.

REFERENCES

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[3] Mao Nannan. Classification Research on Alzheimer's Disease Based on Multimodal Feature Fusion [D]. Dalian Maritime University. 2021.
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[5] Chang Jucai, Qi Pengfei, and Chen Xiao. Multi-condition Rock Hardness Recognition for Roadheader Based on Feature Selection and Random Forest [J]. Journal of China Coal Society, 2023, 48(2): 1070-1084.
[6] Tao Jintao, Zhang Nannan, Chang Jinyu, et al. Three-dimensional Ore Deposit Prediction Research Based on Logistic Regression for the Honghai Ore Deposit in the East Tianshan Mountains [J]. Xinjiang Geology, 2022 (040-001).

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