Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital
Ojo Timothy Ayodele,
Alabi Olatayo Olusegun,
Oyegoke Adiat Odunayo,
Ayinde Liasu Adekunle,
Ogunwole Bolawa Adijat
Issue:
Volume 6, Issue 2, June 2020
Pages:
53-55
Received:
25 April 2020
Accepted:
21 May 2020
Published:
10 August 2020
Abstract: In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x1), Urea (x2), temperature (x3), Fasting blood sugar (x4), Body mass index (x5), and marital status (x6). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x1+0.008 x2-0.897 x3-0.021 x4-0.017 x5-2.872 x6. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.
Abstract: In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and...
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Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management
Ayodeji Ibitoye,
Olufade Onifade
Issue:
Volume 6, Issue 2, June 2020
Pages:
56-59
Received:
17 June 2020
Accepted:
2 July 2020
Published:
20 August 2020
Abstract: The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.
Abstract: The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a poten...
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