0000000000185477
AUTHOR
Amir Hajjam El Hassani
How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?
This chapter introduces the characteristics of e-care platform and the concept of ontology which helps the reader understand the system that will implement big data tools for its migration while also focusing on focuses on the most popular systems in the Hadoop ecosystem, emphasizing MapReduce and Spark.
Heart Failure Occurrence: Mining Significant Patterns and 10 Days Early Prediction
Electronic health records containing patient’s medical history, drug prescription, vital signs measurements, and many more parameters, are being frequently extracted and stored as unused raw data. On the other hand, machine learning and data mining techniques are becoming popular in the medical field, providing the ability to extract knowledge and valuable information from electronic health records along with accurately predicting future disease occurrence. This chapter presents a study on medical data containing vital signs recorded over the course of some years, for real patients suffering from heart failure. The first significant patterns that come along with heart failure occurrence are…
Stream processing in Big Data for e-health care
In this chapter, we will present the stream processing and batch processing. Besides, we will conduct a qualitative comparison of the most popular data processing systems, namely Storm and Spark streaming. We will describe their respective underlying bases and the functionalities they provide and discuss how they can be introduced into e-health care analysis programs.
Sequential Mining Classification
Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …