Road traffic accident is one of the major problems facing the world. The carnage on Ghana’s roads has raised road accidents to the status of a ‘public health’ threat. The objective of the study is to identify factors that contribute to accident severity using an ordinal regression model to fit a suitable model using the dataset extracted from the database of Motor Traffic and Transport Department, from 1989 to 2019. The results of the ordinal logistic regression analyses show that the nature of cars, National roads, over speeding, and location (urban or rural) are significant indicators of crash severity. Strategies to reduce crash injuries should physical enforcement through greater Police presence on our roads as well as technology. There is also the need to train drivers to be more vigilant in their travels especially on the national roads and in the urban areas. The Recommendation is, a well thought out and contextualised written laws and sanctioned schemes to monitor and enforce strict compliance with road traffic rules should be put in place.

The existing face‐to‐face (F2F) method of counseling in most of Ghana’s public tertiary institutions is accompanied by difficulties and challenges, such as stigmatization, depression among students, and limited counseling access. Our research methodology utilized a questionnaire research instrument as a pathway for substantiating the development of our proposed computerized software solution. A total of 434 students and 10 counselors from public universities in Accra responded to the questionnaires. To corroborate the responses received and provide an appropriate solution, we proposed, developed, and implemented an online (web‐based) interactive counseling application for relevant stakeholders in the case study universities, through a Design Science Research (DSR) approach. Implementation results through computerized evaluation showed that our proposed online counseling system is highly beneficial, and it provides optimum solutions to the challenges.

Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand‐driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location‐aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web‐based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F‐measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE).

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

1 Statistical literacy among people is a key skill expected of every individual in any data-driven society, and it has become necessary for both the young and old to appreciate numeracy and literac...