Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

Testing of combined heating systems for small houses: Improved procedures for whole system test methods : Deliverable 2.3

Dynamic system test methods for heating systems were developed and applied by the institutes SERC and SP from Sweden, INES from France and SPF from Switzerland already before the MacSheep project started. These test methods followed the same principle: a complete heating system – including heat generators, storage, control etc., is installed on the test rig; the test rig software and hardware simulates and emulates the heat load for space heating and domestic hot water of a single family house, while the unit under test has to act autonomously to cover the heat demand during a representative test cycle. Within the work package 2 of the MacSheep project these similar – but different – test methods were harmonized and improved. The work undertaken includes:  • Harmonization of the physical boundaries of the unit under test. • Harmonization of the boundary conditions of climate and load. • Definition of an approach to reach identical space heat load in combination with an autonomous control of the space heat distribution by the unit under test. • Derivation and validation of new six day and a twelve day test profiles for direct extrapolation of test results.   The new harmonized test method combines the advantages of the different methods that existed before the MacSheep project. The new method is a benchmark test, which means that the load for space heating and domestic hot water preparation will be identical for all tested systems, and that the result is representative for the performance of the system over a whole year. Thus, no modelling and simulation of the tested system is needed in order to obtain the benchmark results for a yearly cycle. The method is thus also applicable to products for which simulation models are not available yet. Some of the advantages of the new whole system test method and performance rating compared to the testing and energy rating of single components are:  • Interaction between the different components of a heating system, e.g. storage, solar collector circuit, heat pump, control, etc. are included and evaluated in this test. • Dynamic effects are included and influence the result just as they influence the annual performance in the field. • Heat losses are influencing the results in a more realistic way, since they are evaluated under "real installed" and representative part-load conditions rather than under single component steady state conditions.   The described method is also suited for the development process of new systems, where it replaces time-consuming and costly field testing with the advantage of a higher accuracy of the measured data (compared to the typically used measurement equipment in field tests) and identical, thus comparable boundary conditions. Thus, the method can be used for system optimization in the test bench under realistic operative conditions, i.e. under relevant operating environment in the lab.   This report describes the physical boundaries of the tested systems, as well as the test procedures and the requirements for both the unit under test and the test facility. The new six day and twelve day test profiles are also described as are the validation results.