A Fuzzy Intelligent Framework for Healthcare Diagnosis and Monitoring of Pregnancy Risk Factor in Women

The harmful effect of pregnancy risk factors to the body cannot be underestimated. Pregnancy risk factors are all the aspects that endanger the life of the mother and the baby. The infant mortality rates are still high in developing countries despite national and international efforts to redress this problem of pregnancy risk factors. The operations of the prediction of pregnancy risk factors are complex and risky due to fluctuation in the diagnosis of these risk factors. This is due to the vagueness, incompleteness, and uncertainty of the information used. Also, the health population index, which is based primarily on the result of medical research, has a strong impact upon all human activities. Medical experts are considered best fit for interpretation of data and setting the diagnosis, but medical decision making becomes a very hard activity because the human experts, who have to make decision, can hardly process the huge amount of data. This paper presents a fuzzy logic model for the diagnosis and monitoring of pregnancy risk factor for in order to make accurate reasoning with huge amount of uncertain knowledge. The model is developed based on clinical observations, medical diagnosis and the expert’s knowledge. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. The model will provide decision support platform to pregnancy risk factor researchers, physicians and other healthcare practitioners in obstetrical. The study will also guide healthcare practitioners in obstetrical and gynecology clinic regions in educating the women more about the pregnancy risk factors and encouraged them to start antenatal clinic early in pregnancy. Keyword: Fuzzy inference System, Artificial Intelligence, Expert System, Pregnancy risk factors, Infant mortality, Pregnancy outcom

Cloud enabled data analytics and visualization framework for health-shocks prediction

In this paper, we present a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services (AWS) integrated with geographical information systems (GIS) to facilitate big data capture, storage, index and visualization of data through smart devices for different stakeholders. In order to develop a predictive model for health-shocks, we have collected a unique data from 1000 households, in rural and remotely accessible regions of Pakistan, focusing on factors like health, social, economic, environment and accessibility to healthcare facilities. We have used the collected data to generate a predictive model of health-shock using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting health-shocks. The evaluation of the proposed system in terms of the interpret-ability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold cross-validation of the data samples shows above 89% performance in predicting health-shocks based on the given factors

What We Know So Far: Artificial Intelligence in African Healthcare

Healthcare in Africa is a complex issue influenced by many factors including poverty, lack of infrastructure, and inadequate funding. However, Artificial intelligence (AI) applied to healthcare, has the potential to transform healthcare in Africa by improving the accuracy and efficiency of diagnosis, enabling earlier detection of diseases, and supporting the delivery of personalized medicine. This paper reviews the current state of how AI Algorithms can be used to improve diagnostics, treatment, and disease monitoring, as well as how AI can be used to improve access to healthcare in Africa as a low-resource setting and discusses some of the critical challenges and opportunities for its adoption. As such, there is a need for a well-coordinated effort by the governments, private sector, healthcare providers, and international organizations to create sustainable AI solutions that meet the unique needs of the African healthcare system.Comment: 8 pages, 1 figure, AAAI-23 conference in Washington, DC, International Workshop on the Social Impact of AI for Africa(SIAIA

An evaluation and estimation of risk factors associated with cholera : case study of registered patients in Raymond Mhlaba local municipality, South Africa

Background: Cholera is an acute infectious disease of the small intestine caused by the bacterium called Vibrio cholerae, which has two serogroups01and 0139which is also known as choleragenic V. cholerae. This disease is characterized by profuse watery diarrhoea and severe dehydration which can lead to death of both adult and children if treatment is not promptly given. Cholera is spread through ingestion of V. cholera contaminated water and food. Cholera has displayed global presence more than seven times and caused tremendous disaster to humankind. Method: This was a retrospective study among patients with cholera within the period of ten years (2005 to 2015) and the total number of patients was 106. The target population for this study were patients at Raymond Mhlaba Local Municipality who attended Victoria hospital and were diagnosed with Vibrio cholerae species with respect to sources of water and non-water sources during the mentioned period. A multivariate Logistic regression was used to determine the risk factors of cholera and comparison was made in the treatment of cholera outcomes for factors which were statistically significant at P < 0.05. Results: The median age was 24.5 (IQR: 7.0-44.8) for all respondents with cholera. Patients within the age range of 26-40 and 41-55 were found to have a higher risk of cholera (2.20, 95percent CI: 1.51, 4.22) and (1.13, 95percent CI: 0.61, 2.01) respectively. The risk of cholera was considerably higher among the black race (2.51, 95percent CI: 1.52, 4.31) compared to the coloured (1.33, 95percent CI: 0.75, 3.713). Patients who used source of water supply from carrier/Tanker and Dam/River had higher increased risk of contracting cholera (1.71, 95percent CI: 0.92, 3.62) and (2.61, 95percent CI: 1.38, 4.25) respectively compared to patients that used other sources of water. Home, party and restaurant as places patients had eaten 24 hours earlier before the onset of cholera were associated with increased risk of severe cholera. Patients who shared toilet facilities had increased risk of cholera (0.91, 95percent CI: 0.47, 1.62) compared to the ones who used private toilet. Those patients who did not practice hand washing had an increased risk of contracting cholera (1.45, 95percent CI: 0.88, 2.12) compared to the ones who washed their hands. When Logistic regression was carried out, the following risk factors were found to be statistically significant in causing cholera at 5percent significance level; Age ( 26-40), gender, level of education, marital status, sources of water supply, place eaten in the last 24 hours before onset of cholera, type of toilet used and hand washing. Conclusion: Improvement in level of education, sources of water supply, place of last eaten before cholera sickness, toilet facilities, hand washing practices are key risk factors for cholera disease and hospitalization among patients in Raymond Mhlaba local Municipality, Eastern Cape. The strong association between water and sanitation highlights the need for a more thorough assessment of potential waterborne exposures and the risk faced by family members suffering from cholera infection cases and may warrant renewed research regarding the use of targeted chemoprophylaxis in endemic rural settings

Scientific visualization of multi-temporal remotely-sensed data for monitoring drought-related famine conditions : nutritional, socio-economic & climatic vulnerability in Sudan's Gezira

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1999.Vita.Includes bibliographical references (leaves 186-202).This study addresses the design and deployment constraints and potential utility of an emerging analytical concept for planning adaptive response and mitigation of the regional impact of global climate change, within the context of a complex region in Sudan, with multiple biogenic and anthropogenic vulnerabilities. The specific conceptualization is referred to herein as the Temporal Analysis, Reconnaissance, and Decision Integration System (TARDIS). TARDIS is conceived as a composite planning tool, incorporating virtual temporal analysis, virtual spatial analysis, change detection for archival remotely-sensed data, trend extrapolation, generation of alternative future what-if scenarios and integration with both quantitative and rule-based decision-support. The rationale for developing the specifications for the TARDIS proof-of-concept is the observation that decisions concerning complex phenomena, involving multiple intractable problems, deserve to be made in an information-rich environment. Moreover, it is contended that such decisions could benefit both from an historical perspective and from the luxury of a comparative visualization of possible future outcomes of past trends, current policies and putative what-if constructs. The broad parameters for multi-variable factors affecting food security and the potentially significant regional impact of global climate change on Sudan's Gezira are presented. Also described are the potential contributions of the TARDIS in supporting planners and decision-makers, whose decisions might benefit from visualization of archival satellite data and from visualization of alternative future scenarios. I am primarily concerned with a triad of issues, in the order presented, and their interaction with one another: > FOOD SECURITY, WITH SPECIFIC REFERENCE TO THE SUDAN > GLOBAL CLIMATE CHANGE AND ITS IMPACT ON FOOD PRODUCING REGIONS, SUCH AS SUDAN'S GEZIRA > VISUALIZATION TECHNIQUES FOR TIME-SERIES SATELLITE DATA TO SUPPORT DECISION ANALYSIS, UNDER CONDITIONS OF ENVIRONMENTAL COMPLEXITY, TYPIFIED BY THE SUDAN CASE STUDY Under this broad rubric, I seek to define a discrete area of concentration, namely, the articulation of design specifications for a proof-of-concept composite prototype decision support tool, incorporating scientific visualization of remotely sensed data. Although this tool potentially has generic applicability to decision-making and planning within diverse disciplines and geographic locations, the intended application, herein, is as a tool supporting decisions regarding future food security for Sudan's Gezira agricultural area, with specific reference to food crop, dhurra, (Sorghum bicolor) and cash crop, long staple cotton, (Gossypium Barakatensis) sustainability, under anticipated hotter and more arid climate conditions. The objective of applying this tool to the Sudanese context is to facilitate long-term planning and decision-making related to food security issues in the Gezira, given the climatological threat of future increased temperature and decreased precipitation. Accordingly, the first demonstration of the TARDIS proof-of-concept will be a simulated test run (STR) of data pertinent to Sudan's Gezira. The results of this STR will be evaluated in Chapter 4, and, based upon the outcome, recommendations for regional adaptive response are offered and refinements and modifications will be suggested to improve TARDIS utility and functionality. This research seeks to establish a role for state-of-the-science visualization of remotelysensed data, as a tool for planning adaptive responses to impending climatic change and to food insecurity. Moreover, the study hypothesizes that informed decision-making and policy formulation can be facilitated, through an analysis of the archival satellite and meteorological data for Sudan's Gezira, combined with an assessment of selected current conditions (e.g. civil war, political instability and international isolation, insect infestation in the irrigated agricultural schemes, prevalence of diseases such as schistosomiasis, malaria and cholera), and with an analysis of alternative future what-if scenarios. Potential vested constituents for such technology include various bi-lateral and multi-lateral entities with trade, aid or oversight relationships with Sudan. For purposes of this study, one such agency has been selected, namely, the Global Terrestrial Observing System (GTOS), a newly established umbrella entity within the United Nations, whose mission is "to provide policy makers, resource managers and researchers with the data they need to detect, quantify, locate and understand changes (especially reductions) in the capacity of terrestrial ecosystems to support sustainable development." Accordingly, GTOS has been identified as a potential TARDIS enduser, under the proposed auspices of the prototypical joint Food and Agricultural Organizattion (FAO)/ World Food Programme (WFP) annual Crop Survey and Nutritional Needs Assessment Mission to Sudan.by Gilbert Leonard Rochon, III.Ph.D

A reference machine learning model for prediction of cholera epidemics based-on seasonal weather changes linkages in Tanzania

A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of hilosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe Cholera epidemic remains a public threat throughout history, affecting vulnerable populations living with unreliable water and sub-standard sanitary conditions. Studies have observed that the occurrence of cholera has also, strong linkage with seasonal weather patterns. Over the past decades, there have been great achievements in developing cholera epidemic models which have focused on using mathematical techniques. However, most existing prediction systems have some challenges such as lack of flexibility, not user friendly, in-effective and also, lack integration of essential weather variables. In addition, the use of advanced technology such as machine learning (ML) have not been explicitly deployed in modeling cholera epidemics in developing countries including Tanzania; due to the challenges that come with its datasets such as missing-information, data-inconsistency, imbalance-class and other uncertainties. The aim of this work was to overcome and complement the existing challenges of cholera epidemic models by taking the advantages of ML techniques. Hence, by developing an ML model that is capable of predicting cholera epidemic outbreaks based-on seasonal weather changes linkages in Tanzania. Secondary datasets from Tanzania Meteorological Agency (TMA), the Ministry of Health and Social Welfare, and Dar es Salaam Water and Sewerage Authority (DAWASCO) were used. Then, Adaptive Synthetic Sampling Approach (ADASYN) and Principal Component Analysis (PCA) were applied to restore sampling balance and dimensions of the dataset. In order to determine which ML algorithms were best able to predict (yes/no) whether cholera epidemic would occur given the weather variables, ten classification algorithms were evaluated using F1-score, sensitivity and balancedaccuracy metrics. The Friedman-test was then used to determine whether the performance of the models was statistically significant. Results showed that Random Forest, Bagging, and ExtraTree classifiers had the best performance, with 74%, 74.1% and 71.9% accuracy respectively. The ensemble method of model fine-tuning was then applied in order to obtain one model from the three, and an overall accuracy of 78.5% was achieved. Lastly, a model evaluation process was performed on the selected final model. The model validation process involved four processes: The first evaluation process re-ran the final model using the same dataset but without the weather variables; which resulted into confirming that the model with weather variables to have higher performance compared to the model without the weather variable. The second evaluation process re-ran the model-development procedure using datasets from Tanga and Songwe regions in order to illustrate on how the adaptive reference model can be referenced by other researchers. The third and fourth model evaluation involved mixed-design approach of quantitative and qualitative methods using focus group discussions and interviewer-administered questionnaires with 500 and 20 stakeholders (including; medical officers, epidemiological analysts, nurses, environmental experts, ICT experts and cholera patients) respectively. The results of the third evaluation process proved that 90% of the responses agreed that, the developed model is robust and appropriate to work in least developing countries towards effective prediction of cholera epidemics. Whereas, the results of the fourth evaluation process proved also that cholera ML model is better in terms of their usability, expandability and computational complexity compared to the cholera statistical models. Overall, the study improved our understanding of the significant roles of ML strategies in health-care data. However, the study could not be treated as a time series problem due to data collection bias such as data-inconsistency in terms of time. The study recommends a review of health-care systems in order to facilitate quality data collection and further deployment of ML techniques in the health sector in Tanzania

Integrative Assessment and Modelling of the Non Timber Forest Products Potential in Nuba Mountains of Sudan by Field Methods, Remote Sensing and GIS

Pressure imposed at any one place or point in time results in a complexity of spatial and temporal interactions within topographical ecosystems. It can be propagated through the system and may have implications for future ecosystem functions over a wide array of various spatial and temporal scales. Under conditions of wars and other socio-economic conflicts, these processes are most forceful in developing countries amidst declining economic growth, lack of awareness, deterioration of ecosystem services, loss of existing traditional knowledge bases and weak governance structures. Forests are an essential part of ecosystem services, not only as a resource but as a contributor to biological systems as well. They represent one of the most important sectors in the context of Environmental Change (EC), both from the point of mitigation as well as adaptation. While forests are projected to be adversely impacted under EC, they are also providing opportunities to mitigate these changes. Yet this is one of the least understood sectors, especially at the regional level - many of its fundamental metrics such as mitigation potential, vulnerability and the likely impacts of EC are still not well understood until now. Thus, there is a need for research and field investigations into the synergy of mitigation and adaptation so that the cost of addressing EC impacts can be reduced and the co-benefits can be increased. The aim of this study is to focus particularly on forest-based ecosystem services and to use forests as a strategy for inducing environmental change within the Nuba Mountains in Sudan, specifically for systems in poor condition under EC, and furthermore to explore forests as an entry point for investigating the relationship between urban and rural development and ecosystem services. In addition, the aim is also to raise understanding of the relations between patterns of local-level economic and demographic changes, the nature and value of local ecosystem services, and the role of such services in increasingly interlinked urban and rural livelihood systems. The methodology applied in the current research is three-pronged: a formal literature review, a socio–economic survey (based on semi-structured interviews of household heads via Rapid Rural Appraisal (RRA), with a focus on group discussions, informal meetings, free listening and key informant techniques), and multitemporal optical satellite data analysis (i.e. Landsat and RapidEye). Landsat imagery was utilized to gather the spatial characteristics of the region and to study the Land Use/Land Cover (LU/LC) changes during the period from 1984 to 2014. Meanwhile, RapidEye imagery was used to generate the tree species distribution map. Qualitative and quantitative techniques were applied to analyze socio-economic data. Moreover, Food Consumption Score (FCS) was used to gauge both diversity and frequency of food consumption in surveyed areas. Geographic object-based image analysis (i.e. K-Nearest Neighbour classifier and knowledge-based classifiers) based on a developed model of integrated features (such as vegetation indices, DEM, thematic layers and meteorological information) was applied. Post Classification Analysis (PCA) as well as Post Change Detection (PCD) techniques were used. Hotspot analysis was conducted to detect the areas affected by deforestation. Furthermore, Ordinary Least Squares regression (OLS), Autocorrelation (Moran's) analysis, and Geographically Weighted Regression analyses (GWR) were applied to address the interaction of the different socioeconomic/ecological factors on Non Timber Forest Products (NTFPs) collection and to simulate the dependency scenarios of NTFPs along with their impact on poverty alleviation. Additionally, simulation was performed to estimate the future forest density and predict the dependency on forest services. An increasing impact of intensive interactions between the rural and urban areas has long been acknowledged. However, recent changes in the global political economy and environmental systems, as well as local dynamics of the study area driven by war, drought and deforestation, have led to an increasing rapidity and depth in rural transformation, as well as to a significant impact on urban areas. Like most environmental problems, the effects of these drivers are complex and are stressed diversely across different geographic regions by the socio-political processes that underlie recent economic and cultural globalization. These interactions and processes have increasingly brought rapid changes in land cover, social, institutional and livelihood transformation across broad areas of South Kordofan. Moreover, the study unveils new dynamics such as high rates of migration and mobility by the indigenous population and the increasing domination of market-centric livelihoods in many villages that were once dominated by rural agricultural and natural resourcesbased socio-economic systems. Furthermore, the research highlights the significant roles of NTFPs and trees in contributing to Nuba Mountains’ economic development, food security and environmental health, indicating which requirements need to be addressed in order to improve these potentials. The study proves that drawing on a wide range of these products for livelihood strengthens rural people’s ability to deal with and adapt to both EC and extreme events. Moreover, the results underline the importance of participatory approaches of rural women and their impact on NTFPs management with recommendations of more emphasis on potential roles and the ability of women to participate in public fora. Furthermore, the study shows that the use of high-resolution satellite imagery, integrated with model-based terrestrial information, provides a precise knowledge about the magnitude and distribution of LU/LC patterns. These methods can make an important contribution towards a better understanding of EC dynamics over time. The study reveals that more information exchange is needed to inform actors and decision makers regarding specific experiences, capacity gaps and knowledge to address EC. Subsequently, new policies and strategies are required to much more specifically focus on how to deal with consequences of longer-term EC rather than with the impacts of sudden natural disasters