Name
Abstract | ||
|---|---|---|
AbstractSensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transmitted to pregnant woman to make quick decisions; with the intervention of her doctor; through an android mobile application that we created. As well, she receives recommendations based on her behavior. We used real-world data to validate the system and assess its performance and effectiveness. Experiments were made using the Big Data Platform Databricks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.4018/IJDST.2019100104 | Periodicals |
Keywords | Field | DocType |
Big Data, Cluster, Data Mining, Databricks, Kmeans, Miscarriage Prediction, Spark | Data science,Health care,Computer science,Miscarriage,Big data,Distributed computing | Journal |
Volume | Issue | ISSN |
10 | 4 | 1947-3532 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiba Asri | 1 | 0 | 0.34 |
| Hajar Mousannif | 2 | 82 | 11.01 |
| Hassan Al Moatassime | 3 | 0 | 0.34 |