Abstract
Dementia is an incurable disease that affects a large part of the population of elders and more than 21% of the elders suffering from dementia are exposed to polypharmacy. Moreover, dementia is very correlated with diabetes and high blood pressure. The medication adherence becomes a big challenge that can be approached by analyzing the daily activities of the patients and taking preventive or corrective measures. The weakest link in the pharmacy chain tends to be the patients, especially the patients with cognitive impairments. In this paper we analyze the feasibility of four classification algorithms from the machine learning library of Apache Spark for the prediction of the daily behavior pattern of the patients that suffer from dementia. The algorithms are tested on two datasets from literature that contain data collected from sensors. The best results are obtained when the Random Forest classification algorithm is applied.
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Acknowledgement
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI UEFISCDI and of the AAL Programme with co-funding from the European Union’s Horizon 2020 research and innovation programme project number AAL 44/2017 within PNCDI III [1].
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Moldovan, D. et al. (2019). Spark-Based Classification Algorithms for Daily Living Activities. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_8
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DOI: https://doi.org/10.1007/978-3-319-91189-2_8
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