Haziq Mumtaz

Academic

Career

Verbatim text

JAIZA 

Standardized healthcare documents have a high adoption rate in today's hospital setup. The complexity and size of these documents compounds the issue of handling them, which is why applying big data techniques is necessary. The nature of big data techniques can trigger semantic loss in health documents when they are partitioned for processing and are disseminated to two different nodes of the big data cluster for processing. This semantic loss is critical with respect to clinical use due to its sensitive nature. Especially when we consider making predictions of different diseases in patients. If data is lost during model training the accuracy of predictions is affected and may decrease severely. In our research we have seen how we can preserve the data and the accuracy when making predictions of heart disease. 
Features include: 
 Semantic Preservation of standardized medical records in Big data 
 Predictive analytics of heart diseases. 
 Research on the effect of preserved data and non-preserved (normal) data on predictive analytics. 
 Research on the effect of feature selection on prediction of heart diseases. 
 Web Application for heart prediction on real time standardized medical records. 




















Technology Used: 
Java, Hadoop 2.7, Weka 3.6, React  
, Eclipse 
Supervisor Name: 
Mr. Shujaat Hussain 
Group Members:   
Abdul Baseer Ahamd Siddiqui (i16 - 0067) 
Haziq Mumtaz (i16 - 0238)

AI enrichment