Ensemble-based machine learning for predicting sudden human fall using health data
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2021
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Mathematical Problems in Engineering
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Zusammenfassung
We attempt to predict the accidental fall of human beings due to sudden abnormal changes in their health parameters such as blood pressure, heart rate, and sugar level. In medical terminology, this problem is known as Syncope. The primary motivation is to prevent such falls by predicting abnormal changes in these health parameters that might trigger a sudden fall. We apply various machine learning algorithms such as logistic regression, a decision tree classifier, a random forest classifier, K-Nearest Neighbours (KNN), a support vector machine, and a naive Bayes classifier on a relevant dataset and verify our results with the cross-validation method. We observe that the KNN algorithm provides the best accuracy in predicting such a fall. However, the accuracy results of some other algorithms are also very close. Thus, we move one step further and propose an ensemble model, Majority Voting, which aggregates the prediction results of multiple machine learning algorithms and finally indicates the probability of a fall that corresponds to a particular human being. The proposed ensemble algorithm yields 87.42% accuracy, which is greater than the accuracy provided by the KNN algorithm.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
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ISBN
ISSN
1563-5147
1024-123X
1024-123X
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
SAXENA, Utkarsh, Soumen MOULIK, Soumya Ranjan NAYAK, Thomas HANNE und Diptendu Sinha ROY, 2021. Ensemble-based machine learning for predicting sudden human fall using health data. Mathematical Problems in Engineering. 2021. DOI 10.1155/2021/8608630. Verfügbar unter: https://doi.org/10.26041/fhnw-7111