Poisson-New Quadratic-Exponential Distribution

Binod Kumar Sah, Suresh Kumar Sahani — Sat, 06 Apr 2024 02:01:56 +0000

This proposed distribution is a discrete compound probability distribution with only one parameter. To get this distribution, Poisson distribution has been mixed with the New Quadratic-Exponential distribution of Sah (2022). Hence, it is named as “Poisson-New Quadratic-Exponential Exponential Distribution (PNLED)”. The important statistical characteristics needed to check the validity of this distribution have been derived and clearly explained. To check the validity of the theoretical works of this distribution, while using goodness of fit on some over-dispersed count data, what we have been found that this distribution seems a better alternative of Poisson-Lindley distribution (PLD) of Sankaran (1970), Poisson Mishra distribution (PMD) of Sah (2017) and Poisson-Modified Mishra distribution (PMMD) of Sah and Sahani (2023).

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Emmanuel Gbenga Dada, Aishatu Ibrahim Birma, Abdulkarim Abbas Gora — Sat, 20 Apr 2024 00:00:00 +0000

Diabetes mellitus (DM) is a metabolic disease characterised by high levels of glucose in the blood, known as hyperglycemia, that can result in multiple problems within the body. The World Health Organisation (WHO) data for 2021 reveals a substantial increase in the prevalence of diabetes mellitus (DM), with the number of cases rising from 108 million in 1980 to 422 million in 2014. Between 2000 and 2019, there was a 3% increase in mortality rates associated with diabetes, categorised by age. In 2019, DM caused the deaths of more than 2 million people. These concerning figures clearly necessitate an immediate response. An alarming incidence of diabetes among the population of Maiduguri and Borno State inspired this investigation. This research proposed stacking ensemble learning approach to predict the rate of occurrence of diabetes cases in Maiduguri. The paper used different types of regression models to predict the occurrences of diabetes cases in Maiduguri over time. These models included adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE). The performance indicators studied in this work are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE). These metrics were used to assess the effectiveness of both the machine learning and proposed Stacking Ensemble Learning (SEL) approaches. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Experimental results revealed that SEL is a better predictor compared to other machine learning approaches considered in this work with an RMSE of 0.0493; a MSE of 0.0024; and a MAE of 0.0349. It is hoped that this research will help government officials understand the threat of diabetes and take the necessary mitigation actions.

Mikailalsys Journal of Mathematics and Statistics

Poisson-New Quadratic-Exponential Distribution

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State