|Title:||A Decision Support System for medical diagnosis using Data Mining and Machine Learning|
1.Background of the work
Today, due to deployment of wireless sensor devices and its communication through the Internet, a huge amount of sensor data could be achieved continuously for example, for ECG. However, the optimal use of this sensor reading is not properly done since a lot of hidden information is not mine properly. Therefore, discovering of new knowledge related with diseases (e.g. anomalies in ECG signal for Heart disease) is an open and challenging issue. This also effects very much on medical decision support in proper decision-making task. Data mining and machine learning techniques through Artificial Intelligence (AI) approaches could take the advantage to develop a Decision Support System (DSS) for medical diagnosis by identifying new/unknown knowledge and relate them with a specific disease.
The work will develop a DSS for medical diagnosis using AI techniques such as machine learning algorithms and data mining. The main tasks are:
a) Literature Study and State-of-the-art: finding related algorithms for knowledge discovery and DSS, current advancement of the area, identified drawback and limitation of the chosen methods, etc.
b) Implement chosen methods (at least 3 to 4) for knowledge discovery and test them with sensor signals such as ECG, identify best methods with new and unknown knowledge.
c) Identifying best machine learning methods and test them.
d) Developing DSS for diagnosis using best methods and evaluated them with other commonly used methods.
The main outcome of the thesis is to implement a DSS for medical diagnosis based on a literature study, which counts the state-of-the-art of this area as a scientific contribution.
|IDT supervisors:||Mobyen Uddin Ahmed|