Venue : Coral Deira - April 23 ~ 24, 2016, Dubai, UAE

Accepted Papers

  • Chest Dimensions to Predict Individuals' Age - A Case Based Approach
    Andrea Domingues1 Henrique Vicente2,1 Victor Alves1, Joao Neves3 and Jose Neves1, 1University of Minho, Portugal, 2Universidade de Evora, Portugal and 3Drs.Nicolas & Asp, United Arab Emirates
    It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be well thought out, and consequently this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age based on such measurements. Indeed, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Clustering methods were used to distinguish and aggregate historical data in order to reduce the search space, therefore enhancing the cases retrieval process.
  • MLTDD: Use of Machine Learning Techniques for Diagnosis of Thyroid Gland Disorder
    Izdihar Al-muwaffaq and Zeki Bozkus, Kadir Has University, Turkey
    Nowadays, with the huge advancement of technology and science and expansion of computer usage in high-tech calculations, particularly in the field of medicine, intelligence systems and in particular Machine learning algorithm are becoming of big importance in automatic diagnosis and prognosis of different diseases. The diagnosis of thyroid gland disorders by appreciating the data of thyroid in clinical applications comes out as an important classification problem. In this study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using CRT decision tree algorithm. The overall prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.