Browsing by Author "Diwani, Salim Amour"

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    Data mining awareness and readiness in healthcare sector: a case of Tanzania
    (Advances in Computer Science: an International Journal, 2014) Diwani, Salim Amour; Sam, Anael
    Globally the application of data mining in healthcare is great, because the healthcare sector is rich with tremendous amount of data and data mining becoming very essential. Healthcare sector collect huge amount of data in daily basis. Transferring data into secure electronic systems of medical health will saves lives and reduce the cost of healthcare services as well as early discovery of contiguous diseases with advanced collection of data. This study explore the awareness and readiness to implement data mining technology within healthcare in Tanzania public sector. This study is triangulated adopted online survey using Google doc and offline survey using presentation techniques through different hospital and distributed the questionnaires to the healthcare professionals. The issues explored in the questionnaire included the awareness of data mining technology, the level of understanding of, perception of and readiness to implement data mining technology within healthcare public sector. In this study we will analyze the data using SPSS statistical tool.
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    Holistic diagnosis tool for early detection of breast cancer
    (University of Bahrain, 2021) Diwani, Salim Amour; Yonah, Zaipuna Obedi
    Globally, of all cancer diseases, breast cancer is the number one killer in women. The diseases commonly occur in high income countries, but recently there is rapid increase of breast cancer in middle and low income countries in Asia, Latin America and Africa. This is due to increase in life expectancy, increased urbanization and adoption of western cultures. Although, some strategies to reduce the risks of occurrence of breast cancer are being implemented in developed countries, the case in middle and low income countries is that majority of breast cancer patients are affected by the disease due to diagnosis at late stages of the disease. Therefore, early detection of breast cancer is needed to overcome this problem. In this paper, a holistic diagnosis tool for early detection of breast cancer is proposed. The tool is software based utilizing a novel prediction model for breast cancer survivability developed by using available data mining (DM) technologies. Specifically, five popular data mining algorithms (logistic regression, decision tree, support vector machine, K nearest neighbors and random forest) were used to develop the prediction tool using Wisconsin breast cancer data set. In the paper, prediction tool training and test set results are reported. Achieved from the reported work of training sets are classification accuracies of 100%(Decision Tree); 99.8046%(Random Forest); 97.46%(Logistic Regression and Support Vector Machine); 97.07%(K Nearest Neighbors) and for testing sets are classification accuracies of 93.5672%(Decision Tree); 92.9%(Random Forest); 92.39%(Logistic Regression, Support Vector Machine and K Nearest Neighbors). These results are much better than those reported in the literature. The results show that the proposed DM disease prediction tool has potential to greatly impact on current patient management, care and future interventions against the breast cancer disease and through customization even against other deadly diseases.
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    Theoretical factors underlying data mining techniques in developing countries: a case of Tanzania
    (Advances in Computer Science: an International Journal, 2014) Diwani, Salim Amour; Sam, Anael
    Just as the mining of Tanzanite is the process of extracting large block of hard rock's by using sophisticated hard rock mining techniques to find valuable tanzanite glamour, data mining is the process of extracting useful information or knowledge from large un-organized data to enable effective decision making. Although data mining technology is growing rapidly, many IT experts and business consultants may not have a clue about the term. The purpose of this paper is to introduce data mining techniques, tools, a survey of data mining applications, data mining ethics and data mining process.