This Thesis compares CTG classification techniques proposed in the literature and their potential extensions. A comparison between four classifiers previously assessed - Adaboost, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM) - and two proposed classifiers - Bayesian ANN (BANN), Relevance Vector Machine - was conducted using a database of 7,568 cases and two open source databases. The Random Forest (RF) achieved the highest average result and was proposed as a benchmark classifier. The proposal to use model certainty to introduce a third, unclassified, class was investigated using the BANN. An increase in the classification accuracy was demonstrated, however the proportion of cases in the unclassified class was too great to be of practical value. The information content of time series was explored using a Hidden Markov Model (HMM). The average performance of the HMM was comparable with the performance of the benchmark with a smaller distribution across validation folds, demonstrating that time-series information provides more stable estimates of class than stationary methods. Finally a method of system identification was implemented. Significant differences between feature trends and histograms in low pH (< 7.1) and healthy pH (≥ 7.1) cases were observed. These features were used as classifier inputs, and achieved performance similar to existing feature sets. When these features were aligned according the onset of stage 2 labour three unique trend patterns were discovered.

Text in English with abstracts in English, Northern Sotho, Tshivenda and Xitsonga / The purpose of this study was to determine the self-reported competence of newly qualified professional nurses on the critical midwifery skills. The study was conducted in the four public hospitals designated for community service in Gauteng Province. A quantitative descriptive design was used with a structured self-report questionnaire as data collection instrument. Non-probability convenience sampling was used for the study. The sample size was eighty-four newly qualified professional nurses. The Stata 15 software was used for statistical analyses. The researcher used descriptive statistics to describe and synthesize the collected data. The findings revealed that most newly qualified professional nurses had no knowledge in identifying different types of decelerations, management of late and variable decelerations, but had knowledge in most of the skills related to management of third stage of labour. / Maikemišetšo a dinyakišišo tše e be e le go hwetša bokgoni bja go ipega ka nnoši bja baoki bao ba ithutetšego profešene ya booki ka go bokgoni bjo bohlokwa bja pelegišo. Dinyakišišo di dirilwe dipetleleng tše nne tša bohle tšeo di kgethetšwego tirelo ya setšhaba ka Profenseng ya Gauteng. Khwanthitheitif diskriptif disaene ‘Quantitative descriptive design’ e dirišitšwe gammogo le lenaneopotšišo leo le beakantšwego la go ipega ka nnoši ‘structured self-report questionnaire’ bjalo ka sedirišwa sa go kgoboketša bohlatsi. “Non-probability convenience sampling” e dirišitšwe mo go kgetheng banyakišišwa. Bogolo bja sešupo e be e le baoki ba masomeseswai-nne ba baswa bao ba ithutetšego profešene ya booki. “Stata 15 software” e dirišitšwe tshekatshekong ya dipalopalo. Monyakišiši o dirišitše dipalopalo tša tlhalošo ‘descriptive statistics’ go hlaloša le go kopanya ‘data’ yeo e kgobokeditšwego. Ditšweletšo di utollotše gore bontši ba baoki ba baswa bao ba ithutetšego profešene ya booki ga ba na le tsebo ya go hlatha mehuta yeo e fapanego ya diphokotšo, taolo ya diphokotšo tša morago le tša go fetoga, efela ba na le tsebo ka go bokgoni bjo bontši bjoo bo amanago le taolo ya kgato ya boraro ya lešoko. / Ndivho ya ngudo iyi yo vha u wanulusa nḓivho ya vhukoni ha iwe muṋe ya vhaongi vhaswa vha kha ḓi bvaho u phasa vhuongi uri vha na zwikili zwa ndeme zwa vhuongi vhubebisi u swika ngafhi. Ngudo iyi yo itwa kha zwibadela zwiṋa zwa muvhuso zwo ṋewaho u isa tshumelo zwitshavhani kha vunḓu ḽa Gauteng. Kha u kuvhanganya mafhungo muṱoḓisi o shumisa ngona ya u ṱalutshedza ya khwanthithethivi ho ṱanganyiswa na mbudziso dzo dzudzanyiwaho dzi bviselaho khagala kha iwe muṋe (structured self-report questionnaire). Vhunanguludzi ho shumiswaho kha ngudo iyi ho vha “Non-probability convenience”. Tshivhalo tsha vhashelamulenzhe vho nanguludzwaho tsho vha vhaongi vhaswa vha kha ḓibvaho u phasa vha fumalo ina. “The Stata 15 software” ndi tshishumiswa tsho shumiswaho kha u sengulusa mafhungo o kuvhanganywaho. Muṱoḓisisi o shumisa zwisiṱatisitika zwa u ṱalutshedza kha u ṱalutshedza na u dzudzanya mafhungo o kuvhanganyiwaho. Ngudo iyi yo bvisela khagala uri vhunzhi ha vhaongi vhaswa vha kha ḓi bvaho u phasa a vha na nḓivho ya u vhona tshaka dzo fhambanaho dza kurwele kwa mbilu ya ṅwana na u langa u lenga ha u rwa ha mbilu ya ṅwana zwo katela na u sa dzudzanyea fhethu huthihi ha kurwele kwa mbilu ya ṅwana, honeha vha na nḓivho ya zwikili zwi yelanaho na vhulanguli ha tshipiḓa tsha vhuraru tsha u beba. / Xikongomelo xa ndzavisiso lowu i ku kuma vuswikoti lebyi munhu a byi twisisaka hi vaongori lava ha ku thwaselaka tidyondzo ta vuongori eka swikili swa nkoka hi vusungukati. Ndzavisiso lowu wu endliwile eka swibedlhele swa mune swa mani na mani leswi yisaka vukorhokeri evanhwini eka Phurovhinsi ya Gauteng, laha ku tirhisiweke maendlelo ya tinhlayo lama hambanaka na swivutiso ku hlengeleta timhaka. Ku tirhisiwile xiphemu xo karhi xa vanhu ku kuma vuxokoxoko hi mayelano na vona hinkwavo. Xiphemu lexi tirhisiweke i xa nhlayo ya vaongori vo ringana makumenhungu-mune wa vaongori lawa ha ku thwaselaka tidyondzo ta vuongori. Ku tirhisiwile “stata software” ku hlela tinhlayo leti tirhisiweke. Mulavisisi u tirhisile tinhlayo, tinhlayonhlamuselo ku hlamusela no katsakanya mahungu lama a ma hlengeleteke. Leswi kumiweke swi paluxa leswaku vunyingi bya vaongori lava ha ku thwaselaka tidyondzo ta vuongori va hava vutivi byo hambanisa mabelo ya mbilu, ku hlawula ku hlwela no hambana ka mabelo ya mbilu, kambe va na vutivi eka swikili mayelana no lawula xiyimo xa vunharhu xo lumiwa. / Health Studies / M.A. (Nursing)