Early Prediction of Alzheimer’s Disease using Random Forest and E-SS Algorithm
Abstract
Alzheimer Disease (AD) is a neurogenerative disorder that progressively damages the nervous system. Early detection of AD is crucial, as it allows patients to receive therapy at an earlier stage, helping to slow the progression of the disease. This research proposes a model with improved effectiveness and accuracy by combining Random Forest with Entropy-based Subset Selection (E-SS). E-SS is used to identify subsets of parameters that correlate with each other based on entropy. The results show that the combination of Random Forest and E-SS outperforms traditional Random Forest, Decision Tree, SVM, and k-NN models, achieving an accuracy of 95.81% while reducing the number of parameters from 33 to 29. This demonstrates that the proposed algorithm could be applied in the medical field, improving predictive accuracy by eliminating parameters with weak correlations to the disease.
References
[1] E. Area-Gomez and E. A. Schon, “Alzheimer disease,” Adv Exp Med Biol, vol. 997, no. 1, pp. 149–156, 2017, doi: 10.1007/978-981-10-4567-7_11.
[2] R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, “Forecasting the global burden of Alzheimer’s disease,” Alzheimer’s and Dementia, vol. 3, no. 3, pp. 186–191, 2007,
doi: 10.1016/j.jalz.2007.04.381.
[3] R. A. Armstrong, “Risk factors for Alzheimer’s disease,” Folia Neuropathol, vol. 57, no. 2, pp. 87–105, 2019,
doi: 10.5114/fn.2019.85929.
[4] R. Green, RC; Clarke, VC; Thompson, NJ; Woodard, JL; Letz, “Early detection of Alzheimer disease: methods, markers, and misgivings,” Alzheimer Dis Assoc Disord, vol. 11, no. 5, p. S1, 1997,
doi: 10.2217/fnl.10.31.
[5] J. Rasmussen and H. Langerman, “Alzheimer’s Disease – Why We Need Early Diagnosis,” Degener Neurol Neuromuscul Dis, vol. Volume 9, pp. 123–130, 2019, doi: 10.2147/dnnd.s228939.
[6] I. Konenko, “Machine learning for medical diagnosis: History, state of the art and perspective,” Artif Intell Med, vol. 23, no. 1, pp. 89–109, 2001, [Online]. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed5&NEWS=N&AN=2001260608
[7] M. Pal, “Random forest classifier for remote sensing classification,” Int J Remote Sens, vol. 26, no. 1, pp. 217–222, 2005,
doi: 10.1080/01431160412331269698.
[8] L. Breiman, “Random Forests,” 2001.
[9] M. Pal and S. Parija, “Prediction of Heart Diseases using Random Forest,” J Phys Conf Ser, vol. 1817, no. 1, 2021,
doi: 10.1088/1742-6596/1817/1/012009.
[10] O. Shobayo, O. Zachariah, M. O. Odusami, and B. Ogunleye, “Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm,” Analytics, vol. 2, no. 3, pp. 604–617, 2023,
doi: 10.3390/analytics2030034.
[11] M. T. Islam, M. Raihan, F. Farzana, N. Aktar, P. Ghosh, and S. Kabiraj, “Typical and Non-Typical Diabetes Disease Prediction using Random Forest Algorithm,” 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, pp. 1–6, 2020,
doi: 10.1109/ICCCNT49239.2020.9225430.
[12] R. S. Walse, G. D. Kurundkar, S. D. Khamitkar, A. A. Muley, P. U. Bhalchandra, and S. N. Lokhande, “Effective Use of Naïve Bayes, Decision Tree, and Random Forest Techniques for Analysis of Chronic Kidney Disease,” in Information and Communication Technology for Intelligent Systems, T. Senjyu, P. N. Mahalle, T. Perumal, and A. Joshi, Eds., Springer Singapore, 2021, pp. 237–245.
[13] A. Smarra; Francesco; Tjen, Jimmy; D’Innocenzo, “Learning methods for structural damage detection via entropy-based sensors selection,” International Journal of Robust and Nonlinear Control, vol. 32, no. 10, pp. 6035–6067, 2022, doi: 10.1002/rnc.6124.
[14] G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, no. 2, pp. 197–227, 2016, doi: 10.1007/s11749-016-0481-7.
[15] J. Tjen, “Identifikasi Parameter Kualitas Bahan Pangan dengan Metode Entropy-Based Subset Selection (E-SS) (Studi Kasus: Minuman Anggur),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 47–54, 2024, doi: 10.25126/jtiik.20241116850.
[16] R. El Kharoua, “Alzheimer’s Disease Dataset,” 2024, Kaggle.
[17] R. A. Armstrong, “What causes Alzheimer’s disease?,” Folia Neuropathol, vol. 51, no. 3, pp. 169–188, 2013, doi: 10.5114/fn.2013.37702.
[18] M. Kamiya, A. Osawa, I. Kondo, and T. Sakurai, “Factors associated with cognitive function that cause a decline in the level of activities of daily living in Alzheimer’s disease,” Geriatr Gerontol Int, vol. 18, no. 1, pp. 50–56, 2018,
doi: 10.1111/ggi.13135.
[2] R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, “Forecasting the global burden of Alzheimer’s disease,” Alzheimer’s and Dementia, vol. 3, no. 3, pp. 186–191, 2007,
doi: 10.1016/j.jalz.2007.04.381.
[3] R. A. Armstrong, “Risk factors for Alzheimer’s disease,” Folia Neuropathol, vol. 57, no. 2, pp. 87–105, 2019,
doi: 10.5114/fn.2019.85929.
[4] R. Green, RC; Clarke, VC; Thompson, NJ; Woodard, JL; Letz, “Early detection of Alzheimer disease: methods, markers, and misgivings,” Alzheimer Dis Assoc Disord, vol. 11, no. 5, p. S1, 1997,
doi: 10.2217/fnl.10.31.
[5] J. Rasmussen and H. Langerman, “Alzheimer’s Disease – Why We Need Early Diagnosis,” Degener Neurol Neuromuscul Dis, vol. Volume 9, pp. 123–130, 2019, doi: 10.2147/dnnd.s228939.
[6] I. Konenko, “Machine learning for medical diagnosis: History, state of the art and perspective,” Artif Intell Med, vol. 23, no. 1, pp. 89–109, 2001, [Online]. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed5&NEWS=N&AN=2001260608
[7] M. Pal, “Random forest classifier for remote sensing classification,” Int J Remote Sens, vol. 26, no. 1, pp. 217–222, 2005,
doi: 10.1080/01431160412331269698.
[8] L. Breiman, “Random Forests,” 2001.
[9] M. Pal and S. Parija, “Prediction of Heart Diseases using Random Forest,” J Phys Conf Ser, vol. 1817, no. 1, 2021,
doi: 10.1088/1742-6596/1817/1/012009.
[10] O. Shobayo, O. Zachariah, M. O. Odusami, and B. Ogunleye, “Prediction of Stroke Disease with Demographic and Behavioural Data Using Random Forest Algorithm,” Analytics, vol. 2, no. 3, pp. 604–617, 2023,
doi: 10.3390/analytics2030034.
[11] M. T. Islam, M. Raihan, F. Farzana, N. Aktar, P. Ghosh, and S. Kabiraj, “Typical and Non-Typical Diabetes Disease Prediction using Random Forest Algorithm,” 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, pp. 1–6, 2020,
doi: 10.1109/ICCCNT49239.2020.9225430.
[12] R. S. Walse, G. D. Kurundkar, S. D. Khamitkar, A. A. Muley, P. U. Bhalchandra, and S. N. Lokhande, “Effective Use of Naïve Bayes, Decision Tree, and Random Forest Techniques for Analysis of Chronic Kidney Disease,” in Information and Communication Technology for Intelligent Systems, T. Senjyu, P. N. Mahalle, T. Perumal, and A. Joshi, Eds., Springer Singapore, 2021, pp. 237–245.
[13] A. Smarra; Francesco; Tjen, Jimmy; D’Innocenzo, “Learning methods for structural damage detection via entropy-based sensors selection,” International Journal of Robust and Nonlinear Control, vol. 32, no. 10, pp. 6035–6067, 2022, doi: 10.1002/rnc.6124.
[14] G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, no. 2, pp. 197–227, 2016, doi: 10.1007/s11749-016-0481-7.
[15] J. Tjen, “Identifikasi Parameter Kualitas Bahan Pangan dengan Metode Entropy-Based Subset Selection (E-SS) (Studi Kasus: Minuman Anggur),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 47–54, 2024, doi: 10.25126/jtiik.20241116850.
[16] R. El Kharoua, “Alzheimer’s Disease Dataset,” 2024, Kaggle.
[17] R. A. Armstrong, “What causes Alzheimer’s disease?,” Folia Neuropathol, vol. 51, no. 3, pp. 169–188, 2013, doi: 10.5114/fn.2013.37702.
[18] M. Kamiya, A. Osawa, I. Kondo, and T. Sakurai, “Factors associated with cognitive function that cause a decline in the level of activities of daily living in Alzheimer’s disease,” Geriatr Gerontol Int, vol. 18, no. 1, pp. 50–56, 2018,
doi: 10.1111/ggi.13135.
Authors
Kevin Chaily
[1]
“Early Prediction of Alzheimer’s Disease using Random Forest and E-SS Algorithm”, Soc. sci. humanities j., vol. 9, no. 01, pp. 6384–6393, Jan. 2025, doi: 10.18535/sshj.v9i01.1456.
This work is licensed under a Creative Commons Attribution 4.0 International License.