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In this paper, an endeavor has been made to fit three distributions Marshall–Olkin with exponential distributions, Marshall–Olkin with exponentiated exponential distributions and Marshall–Olkin with exponentiated extension distribution keeping in mind the end goal to actualize Bayesian techniques to examine visualization of prognosis of women with breast cancer and demonstrate through utilizing Stan. Stan is an abnormal model dialect for Bayesian displaying and deduction. This model applies to a genuine survival controlled information with the goal that every one of the ideas and calculations will be around similar information. Stan code has been created and enhanced to actualize a censored system all through utilizing Stan technique. Moreover, parallel simulation tools are also implemented and additionally actualized with a broad utilization of rstan.

In this article, a new family of distribution called the alpha power Marshall-Olkin-G (APMO-G) family of distributions was proposed. The new family of distribution provides a better fit for continuous distributions for lifetime processes. The proposed family of distributions extends the existing alpha power transformed family of distributions by an additional parameter. A comprehensive reliability structural properties were derived. The moments, order statistics, hazard rate, and quantile functions, were also examined. The parameters of the proposed APMO-G model were obtained by the maximum likelihood method. Monte Carlo simulation was used to access the performance of the estimators. The goodness-of-fit test statistics were examined by means of two real-life data sets to demonstrate empirical flexibility. The results show that the APMO-G density provides a better fit when compared with other existing models and can serve as a better alternative model in the literature.

This research focused on analyzing and classifying the survival of some breast cancer in-patients in Kwara State, Nigeria. The relationships between some variables that were measured to determine the survival of breast cancer in a cross-sectional study in Nigeria were explored. Data detailing information on gender, age, mode of diagnosis, length of stay, location of breast cancer and the outcome of the treatment on 300 patients admitted based on breast cancer issues were collected at the University of Ilorin Teaching Hospital (UITH), Ilorin, Nigeria between year 2011 and 2016. Descriptive analyses were performed; cluster analysis was performed using k-mean clustering method with the aid of Orange software. Analysis of the cluster is done to reveal the latent patterns that could lead to a better understanding on how some variables could combine to determine the extent of breast cancer survival in Nigeria The scatter plot was used for data visualization while the Silhouette plot showed validates the choice of using only three clusters. Breast cancer survival is associated with the age bracket of the patients. Also, breast cancer survival is dependent on the location of the cancer. In addition, mode of diagnosis was found to be associated with the location of the breast cancer. Furthermore, k-clustering algorithm identified three distinct clusters. The low Silhouette values of obtained in both plots showed that only few clusters are available for this type of data. Louvain clustering revealed the latent patterns that depicts the prevalence of breast cancer in Nigeria.

Breast cancer is one of the leading causes of death in females and survival depends on early diagnosis and treatment. This paper applied machine learning techniques in prediction of breast cancer survival (dead or alive) using age, sex, length of stay, mode of diagnosis and location of cancer as predictors (independent variables). The data was obtained from the outpatient department of the University of Ilorin Teaching Hospital, Ilorin, Nigeria. The sample size of 300 consists of 175 females and 25 males who were admitted at the hospital and treated for breast cancer. The patients were later discharged or died. Adaptive boosting (AdaBoost) performed best out of the data mining models used in the classification in all the three cases where the target class is average over classes, alive or dead. The AdaBoost performed best with the classification accuracy and area under curve (AUC) of 98.3% and 99.9% respectively. Furthermore, a probe on the prediction by AdaBoost showed that the probability of dead due to breast cancer is 0.47, which the length of stay hugely contributed to the high probability, location of breast cancer and mode of diagnosis contributed minimally while age and sex contributed insignificantly. The high probability of breast cancer mortality predicted in this paper is a call for concern as early detection of breast cancer, routine breast examination and breast cancer awareness are crucial in increasing the probability of survival. The results can be used to design a decision support system that can increase the chances of breast cancer survival.