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Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models

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Machine Learning and Big Data Analytics (ICMLBDA 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 401))

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Abstract

The human brain is the most complex part as it controls numerous operations of the human body. For the past few decades, works have been going on to study the working of brain and how to make models that can replicate its working. Artificial intelligence is one such advanced field that trains on data we collect and predicts the output like a human brain. On the other hand, there are many diseases related to brain that have been discovered over time. These diseases progress with the passage of time, and, due to their slow rate of progression, it becomes difficult to detect them at early stage. Alzheimer’s disease (AD) is one of the neurodegenerative diseases which damages the brain permanently and becomes untreatable at the higher stages. Thus, it becomes very important to detect AD at an early stage to increase the life span of the person detected. The main predicament is to detect AD at an early stage and the selection of features responsible for it. The objective of this study is to predict AD at an early stage and to identify the features that facilitate its early prediction using ensemble learning. The proposed multiple ensemble method uses the selected features to achieve an accuracy of 82.40% and with higher ROC.

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Acknowledgments.

https://ida.loni.usc.edu/

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Divjot Singh & Ashutosh Mishra

Authors
  1. Divjot Singh
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  2. Ashutosh Mishra
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Rajiv Misra

  2. Cardiff University, Cardiff, UK

    Rana Omer

  3. Dept. of EE Engineering, University of London, London, UK

    Muttukrishnan Rajarajan

  4. Dept. of ECE, National University of Singapore, Singapore, Singapore

    Bharadwaj Veeravalli

  5. Dept. of Computer Science, Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  6. Dept. of CSE, Indian Institute of Information Technology, Kota, Jawahar Lal Nehru Marg, Rajasthan, India

    Priyanka Mishra

Appendix

Appendix

Dataset attributes

Data values

GC score

0.9299

0.7877

0.8612

0.8331

0.9466

Allele1 – top

3

1

1

3

4

Allele2 – top

3

1

1

3

4

Allele1 – forward

3

4

1

2

4

Allele2 – forward

3

4

1

2

4

Allele1 – AB

2

1

1

2

2

Allele2 – AB

2

1

1

2

2

Position

139,026,180

139,046,223

219,793,146

219,797,929

219,783,037

GT score

0.8931

0.7824

0.8318

0.8117

0.9126

Cluster Sep

1

0.9871

0.514

0.717

0.9218

Theta

0.94

0.043

0.063

0.991

0.994

R

0.897

2.087

0.359

1.251

1.088

X

0.078

1.955

0.327

0.018

0.01

Y

0.82

0.132

0.032

1.234

1.078

X raw

358

6040

1064

217

209

Y raw

3581

691

201

4029

5108

B allele freq

0.9731

0

0.0213

0.9971

1

Log R ratio

−0.1095

0.1914

−0.5333

0.0402

0.2318

Class

1

1

1

1

1

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Singh, D., Mishra, A. (2023). Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models. In: Misra, R., Omer, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Mishra, P. (eds) Machine Learning and Big Data Analytics. ICMLBDA 2022. Springer Proceedings in Mathematics & Statistics, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-031-15175-0_38

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