Mohd Khalit Bin Othman

Conclusion and Future Research Directions

Emergency management involves several complex factors. An analytical decision-making process should be used to assess a health technology based on its performance.

Data Mining Techniques: A Systematic Mapping Review

There is a tremendous amount of content and information on the web, which can be searched for using specific information or patterns. Similarly, for Enterprise Recourse Planning (ERP) systems or normal governmental databases (i.e., health care databases) offer great capabilities. Decisions can be made more effectively by monitoring these systems and controlling the amount of data they contain. Decision-making can be further aided through the utilization of various application clusters or random probability mode. This study highlights the importance of web mining techniques and methods, with an emphasis on its potential and implication in E-Systems, including an investigation of more than fifty thousand high quality articles dated within 1915–2017. Data mining methods work in accordance with browser measurements and historical patterns of information; therefore, there are methods that do not follow forecasted outcomes. Such ineffective methods can be reduced via mathematical probability and probabilistic clusters.

Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia

This paper introduces Dengue Active Surveillance System (DASS) framework for an early warning system of the outbreak. Dengue and dengue hemorrhagic fever are emerging as major public health problems in most Asian countries such as Malaysia. Effective prevention and control programs will depend on improved surveillance. A new approach to active surveillance outlined with emphasis on the inter-epidemic period. The objective is to develop an early warning surveillance system (framework) that can predict epidemic dengue to improve current passive surveillance system available in Malaysia. Basically, the framework introduced data harvesting process from multiple sources as input, data pre-processing using data aggregator and filtering engine, storing large data in repository, analytic engine for analysis and processing the large data, and presentation of the information to the users. The data harvested from two major sources such as weather or flood information, and social media such as build development and dengue symptom using system API, SOAP and others. The data aggregator will aggregate the data from three different types of data such as structured, semi-structured and unstructured data to be stored into the semi-structured database such as MongoDB and NoSQL. The data parse to the filtering engine for filtering and cleaning the data sources using suitable keywords prior to store it in the large data repository. After that, the large data will be processed and analyzed using algorithm or mathematical calculation to determine the expected dengue cases. Then, the processed information will be presented to the users in a form of web or mobile application and other method, for example, short message service (SMS). Finally, the system accuracy will be evaluated based on the comparison study with the traditional passive system.