Abstract
Alzheimer’s disease is one form of dementia. It is characterized by progressive problems in thinking, learning and behavior. Since there is no cure till date, early detection and prediction of progressive stages of the disease may help preclude the severity. In this work, we have used Machine Learning techniques to identify the current stage and predict the progressive stages of this disease. Work in this paper has been carried out in two phases: first, identification of the disease based on 2092 samples, and second, prediction of the progressive stages based on 819 samples. The proposed feature selection technique selects 8 effective biomarkers out of 113 generic biomarkers for identification of disease stage. Our proposed data imputation approach is effective to handle missing records in periodic data used in stage prediction. We have achieved F1 score of 89% for CN, 84% for MCI, 80% for AD stage identification and F1 score of 96% for prediction of each stage of the disease.
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Sarma, M., Chatterjee, S. (2020). Identification and Prediction of Alzheimer Based on Biomarkers Using ‘Machine Learning’. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_23
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DOI: https://doi.org/10.1007/978-981-15-6318-8_23
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