Pre-Processing for Early Alzheimer's Detection Using Data Mining
Keywords:
Data Mining, Healthcare, Medical Data, Biomarkers, Dementia, Pre-processing, Disease, Classification.Abstract
This paper explores the application of the Synthetic Minority Over-sampling Technique combined with Boosting (SMOTEBoost) for pre-processing in early Alzheimer's detection models. The aim is to address class imbalance by generating synthetic instances and boosting the learning process. Using SMOTEBoost in pre-processing improves machine learning algorithms' learning capacities and helps identify early-stage Alzheimer's patterns more accurately. The effectiveness of the suggested strategy is demonstrated by the experimental results, which also highlight how revolutionary early detection techniques could become. The research presented here advances the likelihood of prompt intervention and better patient outcomes by contributing to the continuous efforts to increase the sensitivity and precision of Alzheimer's diagnosis.
Downloads
References
G. S. Lakshmi and P. I. Devi, "Prediction of anti-retro viral for HIV and STD patients using data mining technique," 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputtur, India, 2017, pp. 1-6, doi: 10.1109/ITCOSP.2017.8303084.
R. A. Canessane, R. Dhanalakshmi, B. Pavithra, B. Sasikanth and C. Sandeep, "HUSP Mining Techniques to Detect Most Weighted Disease and Most Affected Diseases for the Healthcare Industry," 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2019, pp. 25-32, doi: 10.1109/ICONSTEM.2019.8918784.
B. V. Baiju and D. J. Aravindhar, "Disease Influence Measure Based Diabetic Prediction with Medical Data Set Using Data Mining," 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), Chennai, India, 2019, pp. 1-6, doi: 10.1109/ICIICT1.2019.8741452.
J. Thomas and R. T. Princy, "Human heart disease prediction system using data mining techniques," 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, 2016, pp. 1-5, doi: 10.1109/ICCPCT.2016.7530265.
S. J. Pasha and E. S. Mohamed, "Bio inspired Ensemble Feature Selection (BEFS) Model with Machine Learning and Data Mining Algorithms for Disease Risk Prediction," 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2019, pp. 1-6, doi: 10.1109/ICCUBEA47591.2019.9129304.
A. Bhagtani, T. Choudhury, G. Raj and M. Sharma, "An efficient survey to detect Alzheimer disease using data mining techniques," 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Tumkur, India, 2017, pp. 64-70, doi: 10.1109/ICATCCT.2017.8389107.
S. Joshi, P. Deepa Shenoy, V. K R and L. M. Patnaik, "Evaluation of different stages of dementia employing neuropsychological and machine learning techniques," 2009 First International Conference on Advanced Computing, Chennai, India, 2009, pp. 154-160, doi: 10.1109/ICADVC.2009.5378199.
M. S. Ali, M. K. Islam, J. Haque, A. A. Das, D. S. Duranta and M. A. Islam, "Alzheimer’s Disease Detection Using m-Random Forest Algorithm with Optimum Features Extraction," 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia, 2021, pp. 1-6, doi: 10.1109/CAIDA51941.2021.9425212.
N. Jannat et al., "Stacking Ensemble Technique for Multiple Medical Datasets Classification: A Generalized Prediction Model," 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, 2023, pp. 1-6, doi: 10.1109/ECCE57851.2023.10101523.
I. Arshad, C. Dutta, T. Choudhury and A. Thakral, "Liver Disease Detection Due to Excessive Alcoholism Using Data Mining Techniques," 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), Paris, France, 2018, pp. 163-168, doi: 10.1109/ICACCE.2018.8441721.
Downloads
Published
How to Cite
Issue
Section
License
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.