Introduction Primary health care in South Africa is predominantly provided by clinics and Community Health Centres (CHC). These primary care facilities are situated in the community to ensure accessibility to care. 1 As part of ensuring quality planning, implementation and transformation of the health system, good knowledge of which cases are prevalent at our primary health facilities is important. Thus the rationale for this study as to know what are the reasons our patients come to our facilities and which diagnoses are made by the attending health care workers based on these reasons. Aim The aim of this study was to describe the spectrum of clinical and non-clinical problems encountered and the diagnoses made at our primary care facilities in the North West province of South Africa. Methods A prospective, cross-sectional survey at 19 Clinics and 5 Community Health Centres in 4 sub-districts of the Ngaka Modiri Molemma District of NW Province, South Africa. The International Classification of Primary Care-Version 2 (ICPC-2) was used to code data on selected days over a 10-month period from patients presenting at the participating clinics and community health centres. Results In total, 5082 patient encounters were recorded of which 3438 (67.7%) were females while 1644 ( 32.3%) were males. The category with highest reasons for encounter (RFE) was the general and unspecific component with 16.5% (n = 1202), followed by the respiratory component at 14.7% (n= 1066) and the cardiovascular component at 12.1% (n=882). The most common diagnoses were in the general component at 16.5% (n= 981) followed by cardiovascular at 16.0% (n= 951) and the respiratory component at 14.5% (n= 865). The average numbers of RFE was 1.4 per encounter among females and 1.5 amongst males. Diagnoses per encounter averaged 1.2 among females and males. Younger people under 40 years of age 67% (n = 3409) and females 68% (n = 3438) made up the majority of encounters. Conclusion Of all the health care facilities surveyed, there were mixtures of RFEs and various diagnoses of mixture of disease components. There were very few patients that came to the facilities for administrative purposes. Majority of the attendees were women. Collection of hypertension medication was the most common reason for encounter (RFE) with uncomplicated hypertension being the commonest diagnoses while psychosocial and problems related to male genitals were the least RFEs. There were more RFEs presented by patients than the diagnoses made by the attending HCWs. The ICPC-2 is a very user friendly tool that can be successfully utilised to monitor encounters and diagnoses at any health care facilities.

Gene expression microarray datasets often consist of a limited number of samples relative to a large number of expression measurements, usually on the order of thousands of genes. These characteristics pose a challenge to any classification model as they might negatively impact its prediction accuracy. Therefore, dimensionality reduction is a core process prior to any classification task. This dissertation introduces the iterative feature perturbation method (IFP), an embedded gene selector that iteratively discards non-relevant features. IFP considers relevant features as those which after perturbation with noise cause a change in the predictive accuracy of the classification model. Non-relevant features do not cause any change in the predictive accuracy in such a situation. We apply IFP to 4 cancer microarray datasets: colon cancer (cancer vs. normal), leukemia (subtype classification), Moffitt colon cancer (prognosis predictor) and lung cancer (prognosis predictor). We compare results obtained by IFP to those of SVM-RFE and the t-test using a linear support vector machine as the classifier in all cases. We do so using the original entire set of features in the datasets, and using a preselected set of 200 features (based on p values) from each dataset. When using the entire set of features, the IFP approach results in comparable accuracy (and higher at some points) with respect to SVM-RFE on 3 of the 4 datasets. The simple t-test feature ranking typically produces classifiers with the highest accuracy across the 4 datasets. When using 200 features chosen by the t-test, the accuracy results show up to 3% performance improvement for both IFP and SVM-RFE across the 4 datasets. We corroborate these results with an AUC analysis and a statistical analysis using the Friedman/Holm test. Similar to the application of the t-test, we used the methodsinformation gain and reliefF as filters and compared all three. Results of the AUC analysis show that IFP and SVM-RFE obtain the highest AUC value when applied on the t-test-filtered datasets. This result is additionally corroborated with statistical analysis. The percentage of overlap between the gene sets selected by any two methods across the four datasets indicates that different sets of genes can and do result in similar accuracies. We created ensembles of classifiers using the bagging technique with IFP, SVM-RFE and the t-test, and showed that their performance can be at least equivalent to those of the non-bagging cases, as well as better in some cases.

Churn prediction is a critical task for businesses to retain their valuable customers. This paper presents a comprehensive study of churn prediction in the telecom sector using 15 approaches, including popular algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and AdaBoost. The study is segmented into three sets of experiments, each focusing on a different approach to building the churn prediction model. The model is constructed using the original training set in the first set of experiments. The second set involves oversampling the training set to address the issue of imbalanced data. Lastly, the third set combines oversampling with recursive feature selection to enhance the model's performance further. The results demonstrate that the Adaptive Boost classifier, implemented with oversampling and recursive feature selection, outperforms the other 14 techniques. It achieves the highest rank in all three evaluation metrics: recall (0.841), f1-score (0.655), and roc_auc (0.793), further indicating that the proposed approach effectively predicts churn and provides valuable insights into customer behavior.

Bacteria identification plays an essential role in preventing health complications and saving patients' lives. The most widely used method to identify bacteria, the bacterial cultural method, suffers from long processing times. Hence, an effective, rapid, and non-invasive method is needed as an alternative. Raman spectroscopy is a potential candidate for bacteria identifi cation due to its effective and rapid results and the fact that, similar to the uniqueness of a human fingerprint, the Raman spectrum is unique for every material. In my lab at the University of Ottawa, we focus on the use of Raman scattering for biosensing in order to achieve high identifi cation accuracy for different types of bacteria. Based on the unique Raman fingerprint for each bacteria type, different types of bacteria can be identifi ed successfully. However, using the Raman spectrum to identify bacteria poses a few challenges. First, the Raman signal is a weak signal, and so enhancement of the signal intensity is essential, e.g., by using surface-enhanced Raman scattering (SERS). Moreover, the Raman signal can be contaminated by different noise sources. Also, the signal consists of a large number of features, and is non-linear due to the correlation between the Raman features. Using machine learning (ML) along with SERS, we can overcome such challenges in the identifi cation process and achieve high accuracy for the system identifying bacteria. In this thesis, I present a method to improve the identifi cation of different bacteria types using a support vector machine (SVM) ML algorithm based on SERS. I also present dimension reduction techniques to reduce the complexity and processing time while maintaining high identifi cation accuracy in the classifi cation process. I consider four bacteria types: Escherichia coli (EC), Cutibacterium acnes (CA, it was formerly known as Propi-onibacterium acnes), methicillin-resistant Staphylococcus aureus (MRSA), and methicillin-sensitive Staphylococcus aureus (MSSA). Both the MRSA and MSSA are combined in a single class named MS in the classifi cation. We are focusing on using these types of bacteria as they are the most common types in the joint infection disease. Using binary classi fication, I present the simulation results for three binary models: EC vs CA, EC vs MS, and MS vs CA. Using the full data set, binary classi fication achieved a classi fication accuracy of more than 95% for the three models. When the samples data set was reduced, to decrease the complexity based on the samples' signal-to-noise ratio (SNR), a classi fication accuracy of more than 95% for the three models was achieved using less than 60% of the original data set. The recursive feature elimination (RFE) algorithm was then used to reduce the complexity in the feature dimension. Given that a small number of features were more heavily weighted than the rest of the features, the number of features used in the classifi cation could be signi ficantly reduced while maintaining high classi fication accuracy. I also present the classifi cation accuracy of using the multiclass one-versus-all (OVA) method, i.e., EC vs all, MS vs all, and CA vs all. Using the complete data set, the OVA method achieved classi cation accuracy of more than 90%. Similar to the binary classifi cation, the dimension reduction was applied to the input samples. Using the SNR reduction, the input samples were reduced by more than 60% while maintaining classifi cation accuracy higher than 80%. Furthermore, when the RFE algorithm was used to reduce the complexity on the features, and only the 5% top-weighted features of the full data set were used, a classi fication accuracy of more than 90% was achieved. Finally, by combining both reduction dimensions, the classi fication accuracy for the reduced data set was above 92% for a signifi cantly reduced data set. Both the dimension reduction and the improvement in the classi fication accuracy between different types of bacteria using the ML algorithm and SERS could have a signi ficant impact in ful lfiling the demand for accurate, fast, and non-destructive identi fication of bacteria samples in the medical fi eld, in turn potentially reducing health complications and saving patient lives.

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The presented text is concerned with the development of Radio Free Europe/Radio Liberty (RFE/RL) in the period from the late 1980s to mid-1990s. The main aspiration of this Diploma Thesis is the introduction of these specific radio projects in a new era with new missions. RFE/RL gained great popularity during the Cold War period as they were powerful tools in the fight against communist regimes in Europe and the Soviet Union. However, with the fall of Iron Curtain, it was generally anticipated that their tasks were complete. Both Services had to fight against these general assumptions and prove that their mission had yet more functions. Thus, they focused on promoting ideas and principles of democratic and liberal societies. This type of educational broadcasting proved to be very much needed in the post-communist countries which sought to implement the norms of the Western world. This paper describes the overall, somewhat complicated story of RFE/RL, its mission and further developments in broadcasting, which were fundamentally influenced by plans for budget cuts and resulted in the relocation of RFE/RL's operations from Munich to Prague. For more thorough understanding of these issues, the paper also draws on broader historical context of RFE/RL's development from the very beginning of its operations in...

The Mara River Basin (MRB) is endowed with pristine biodiversity, socio-cultural heritage and natural resources. The purpose of my study is to develop and apply an integrated water resource allocation framework for the MRB based on the hydrological processes, water demand and economic factors. The basin was partitioned into twelve sub-basins and the rainfall runoff processes was modeled using the Soil and Water Assessment Tool (SWAT) after satisfactory Nash-Sutcliff efficiency of 0.68 for calibration and 0.43 for validation at Mara Mines station. The impact and uncertainty of climate change on the hydrology of the MRB was assessed using SWAT and three scenarios of statistically downscaled outputs from twenty Global Circulation Models. Results predicted the wet season getting more wet and the dry season getting drier, with a general increasing trend of annual rainfall through 2050. Three blocks of water demand (environmental, normal and flood) were estimated from consumptive water use by human, wildlife, livestock, tourism, irrigation and industry. Water demand projections suggest human consumption is expected to surpass irrigation as the highest water demand sector by 2030. Monthly volume of water was estimated in three blocks of current minimum reliability, reserve (>95%), normal (80–95%) and flood (40%) for more than 5 months in a year. The assessment of water price and marginal productivity showed that current water use hardly responds to a change in price or productivity of water. Finally, a water allocation model was developed and applied to investigate the optimum monthly allocation among sectors and sub-basins by maximizing the use value and hydrological reliability of water. Model results demonstrated that the status on reserve and normal volumes can be improved to ‘low’ or ‘moderate’ by updating the existing reliability to meet prevailing demand. Flow volumes and rates for four scenarios of reliability were presented. Results showed that the water allocation framework can be used as comprehensive tool in the management of MRB, and possibly be extended similar watersheds.

Denna studie undersöker användningen av maskininlärningsmodeller för att predicera arbetskraftstrender inom hälso- och sjukvården i Sverige. Med hjälp av en linjär regressionmodell, en Gradient Boosting Regressor-modell och en Exponential Smoothing-modell syftar forskningen för detta arbete till att ge viktiga insikter för underlaget till makroekonomiska överväganden och att ge en djupare förståelse av Beveridge-kurvan i ett sammanhang relaterat till hälso- och sjukvårdssektorn. Trots vissa utmaningar med datan är målet att förbättra noggrannheten och effektiviteten i beslutsfattandet rörande arbetsmarknaden. Resultaten av denna studie visar maskininlärningspotentialen i predicering i ett ekonomiskt sammanhang, även om inneboende begränsningar och etiska överväganden beaktas. / This study examines the use of machine learning models to predict workforce trends in the healthcare sector in Sweden. Using a Linear Regression model, a Gradient Boosting Regressor model, and an Exponential Smoothing model the research aims to grant needed insight for the basis of macroeconomic considerations and to give a deeper understanding of the Beveridge Curve in the healthcare sector’s context. Despite some challenges with data, the goal is to improve the accuracy and efficiency of the policy-making around the labor market. The results of this study demonstrates the machine learning potential in the forecasting within an economic context, although inherent limitations and ethical considerations are considered.