Page 129 - DP-2-3

Design+ ML for predicting Alzheimer’s progression

figures. Alzheimers Dement. 2023;19(4):1598-1695. 14. Goyal C. Data Leakage and Its Effect on the Performance of an
ML Model. Analytics Vidhya; 2021. Available from: https://
doi: 10.1002/alz.13016.
www.analyticsvidhya.com/blog/2021/07/data-leakage-
2. Yang Q, Li X, Ding X, Xu F, Ling Z. Deep learning- and-its-effect-on-the-performance-of-an-ml-model [Last
based speech analysis for Alzheimer’s disease detection: accessed on 2024 Apr 30].
A literature review. Alzheimers Res Ther. 2022;14(1):186.
15. Stekhoven D, Bühlmann P. MissForest--non-parametric
doi: 10.1186/s13195-022-01131-3 missing value imputation for mixed-type data.
3. Shahbaz M, Ali S, Guergachi A, Niazi A, Umer A. Bioinformatics. 2012;28(1):112-118.
Classification of Alzheimer’s Disease Using Machine doi: 10.1093/bioinformatics/btr597
Learning Techniques. In: Proceedings of the 12 International
th
Joint Conference on Biomedical Engineering Systems and 16. Saeys Y, Inza I, Larranaga P. A review of feature
Technologies. Prague, Czech Republic; 2019. p. 296-303. selection techniques in bioinformatics. Bioinformatics.
2007;23(19):2507-2517.
doi: 10.5220/0007949902960303
doi: 10.1093/bioinformatics/btm344
4. AIBL Study ADNI Non-imaging Data. aibl.csiro.au. Available
from: https://aibl.csiro.au/adni/nonimaging.php [Last 17. Malato G. Feature Selection with Random Forest. Your Data
accessed on 2024 Apr 30]. Teacher; 2021. Available from: https://www.yourdatateacher.
com/2021/10/11/feature-selection-with-random-forest
5. ADNI. About. Available from: https://adni.loni.usc.edu/ [Last accessed on 2024 Apr 30].
about [Last accessed on 2024 Apr 30].
18. Tanuja D, Goutam S. Classification of imbalanced big data
6. Rahman M, Prasad G. Comprehensive study on machine using SMOTE with rough random forest. Int J Eng Adv
learning methods to increase the prediction accuracy of Technol. 2019;9:5174.
classifiers and reduce the number of medical tests required
to diagnose Alzheimer’s disease. arXiv (Machine Learning). doi: 10.35940/ijeat.B4096.129219
2022;1-10. 19. Brownlee J. Parametric and Nonparametric Machine Learning
Algorithms. Machine Learning Mastery; 2016. Available
doi: 10.48550/arXiv.2212.00414
from: https://machinelearningmastery.com/parametric-
7. Wirth R, Hipp J. CRISP-DM: Towards a Standard Process and-nonparametric-machine-learning-algorithms [Last
Model for Data Mining. Available from: https://cs.unibo. accessed on 2024 Apr 30].
it/~danilo.montesi/CBD/Beatriz/10.1.1.198.5133.pdf [Last
accessed on 2024 Apr 30]. 20. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.
doi: 10.1023/a:1010933404324
8. Harrikrishna NB. Confusion Matrix, Accuracy, Precision,
Recall, F1 Score. Medium; 2020. Available from: https:// 21. N. Room. How to Use XGBoost for Time-Series Forecasting?
medium.com/analytics-vidhya/confusion-matrix-accuracy- Datadance; 2024. Available from: https://datadance.ai/
precision-recall-f1-score-ade299cf63cd [Last accessed on machine-learning/how-to-use-xgboost-for-time-series-
2024 Apr 30]. forecasting/#step-3-handling-missing-values-and-outliers
[Last accessed on 2024 Apr 30].
9. Scikit-Learn. Scikit-learn: Machine Learning in Python.
Scikit-Learn; 2019. Available from: https://scikit-learn.org/ 22. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting
nd
stable [Last accessed on 2024 Apr 30]. System. In: Proceedings of the 22 ACM SIGKDD
International Conference on Knowledge Discovery and Data
10. Nabel R. PyDrive: Google Drive API Made Easy. PyPI.
Available from: https://pypi.org/project/pydrive [Last Mining - KDD ‘16. 2016. 785-794.
accessed on 2024 Apr 30]. doi: 10.1145/2939672.2939785
11. Bhandari A. Multicollinearity. Causes, Effects and Detection 23. Scikit-Learn. Sklearn.Model_Selection.
Using VIF. Analytics Vidhya; 2023. Available from: RandomizedSearchCV - Scikit-Learn 0.21.3 Documentation.
https://www.analyticsvidhya.com/blog/2020/03/what-is- Scikit-Learn; 2019. Available from: https://scikit-learn.
multicollinearity/#:~:text=multicollinearity%20is%20a%20 org/stable/modules/generated/sklearn.model_selection.
statistical%20phenomenon [Last Accessed on 2024 Apr 30]. RandomizedSearchCV.html [Last accessed on 2024 Apr 30].
12. Turney S. Chi-Square (2) Tests. Types, Formula and 24. Scikit. 3.3. Metrics and Scoring: Quantifying the Quality of
Examples. Scribbr; 2022. Available from: https://www. Predictions - Scikit-Learn 0.23.2 Documentation. Scikit-
scribbr.com/statistics/chi-square-tests [Last accessed on Learn. Available from: https://scikit-learn.org/stable/
2024 Apr 30]. modules/model_evaluation.html#classification-report [Last
accessed on 2024 Apr 30].
13. Pandas.factorize -- Pandas 1.5.3 Documentation. Available
from: https://pandas.pydata.org/docs/reference/api/pandas. 25. Lanier ST. Choosing Performance Metrics. Medium; 2020.
factorize.html [Last accessed on 2024 Apr 30]. Available from: https://towardsdatascience.com/choosing-

Volume 2 Issue 3 (2025) 11 doi: 10.36922/DP025270031