Difference between revisions of "Modelling Health Recommender System using Hybrid Techniques"
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| − | |Summary= | + | |Summary= The goal of this project to develop a health recommender system using existing machine learning techniques. |
|Keywords=Recommendation system, Machine learning, Expert system, | |Keywords=Recommendation system, Machine learning, Expert system, | ||
| − | |Prerequisites=Completed courses in basic machine learning | + | |Prerequisites=Completed courses in basic machine learning are required. |
|Supervisor=Hassan Mashad Nemati, Rebeen Hamad, | |Supervisor=Hassan Mashad Nemati, Rebeen Hamad, | ||
|Level=Master | |Level=Master | ||
Revision as of 16:24, 12 January 2018
| Title | Modelling Health Recommender System using Hybrid Techniques |
|---|---|
| Summary | The goal of this project to develop a health recommender system using existing machine learning techniques. |
| Keywords | Recommendation system, Machine learning, Expert system, |
| TimeFrame | |
| References | |
| Prerequisites | Completed courses in basic machine learning are required. |
| Author | |
| Supervisor | Hassan Mashad Nemati, Rebeen Hamad |
| Level | Master |
| Status | Ongoing |
This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment in order to develop build a recommender system. The primary goal is to plan, develop and test a knowledge-base of health recommendations to be used for automation, increased health and performance.
Our methodology for building the Diagnostics and recommender (D-R) system is sub-divided into three steps: building a model for analyzing the structured
data, building a model for extrapolating the unstructured data and then finally a model that correlates them to produce an appropriate recommendation.
