ORCID Profile
0000-0003-0195-225X
Current Organisations
Kwame Nkrumah University of Science and Technology
,
University of Energy and Natural Resources
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Publisher: Hindawi Limited
Date: 20-02-2023
DOI: 10.1155/2023/1406060
Abstract: Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random s ling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.
Publisher: Hindawi Limited
Date: 14-09-2022
DOI: 10.1155/2022/2939166
Abstract: Background. The use of birth control methods is influenced by complex and competing socioeconomic and demographic factors. Regardless of the complexity of the behavioral approach of women, the utility of contraceptive methods in providing the opportunity of choice is well paired. This study examined the factors driving the usage of contraception and the impact of contraceptive practices on population growth in Pakistan. We also perused the quantification of sociocultural contraceptive use. Methodology. The Pakistan Demographic and Health Survey (PDHS, 2017-18) dataset collected by the National Institute of Population Study (NIPS) was used for all analyses. We applied the frequentist logistic regression model and multinomial logistic regression model in assessing factors impacting contraceptive practices. Bayesian logistic and multinomial regression models were also implemented to compare estimates. The regions and provinces in Pakistan were considered as different clusters, thereby introducing hierarchical structures in the regression model. Results. The study revealed a distinctive highly significant negative effect on contraceptive use and women’s age. The odds ratio (OR) for women aged 25-34, 35-44, and above 44 was 1.242, 1.155, and 0.638, respectively, which shows that the OR of contraceptive use decreases in women aged 25-44. Our study showed the superior performance of the Bayesian model in highlighting disparities among the various cultural streams existing in the country. Estimates of the Bayesian analysis of competing models indicated that the Bayesian models provide powerful estimates compared to the classical models. Conclusion. Our results indicated that contraceptive use is almost relevant to sociodemographic factors (education, age, language, partner, work, etc.). Women with no formal education living in rural areas were not aware of the use of contraception, thereby not using it. Contraceptive use and methods are most probably influenced by the age and the number of children of women. We recommend that high-quality education, counseling, and widespread access to contraceptives should be prioritized in family planning healthcare in all areas of the country, especially rural areas.
No related grants have been discovered for Kassim Tawiah.