Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

Huge amount of Healthcare data are produced every day from the various health care sectors. The accumulated data can be effectively analyzed to identify people's risk from chronic diseases. The process of predicting the presence or absence of the disease and also to diagnosing the various disease using the historical medical data is known as Health Care Analytics. Health care analytics will improve patient care and also the harness practice of medical practitioner. The feature selection is considered as a core aspect of the machine learning which hugely contribute towards the performance of the machine learning model. In this paper symmetry based feature subset selection is proposed to select the optimal features from the Health care data which contribute towards the prediction outcome. The Multilayer perceptron algorithm(MLP) used as a classifier which will predict the outcome by using the features which are selected from the Symmetry-based feature subset selection technique. The chronic disease dataset Diabetes, Cancer, Breast Cancer, and HeartDisease data set accumulated from UCI repository is used to conduct the experiment. The experimental results demonstrate that the proposed hybrid combination of feature selection technique and the multilayer perceptron outperforms in accuracy compare to the existing approaches.

In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naï ve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.

Background: In recent time with the growth of the technology and the business model, customer attrition analysis is considered as a very important metric which decides the revenues and profitability of the organization. It is applicable for all the business domains irrespective of the size of the business even including the start-ups. Because about 65% revenue for the organization comes from the existing customer. The goal of the customer attrition analysis is to predict the customer who is likely to exit or churn from the current business organization. In this research work, the literature review is carried out to explore the related work which has been already carried out in the field of customer attrition analysis. The literature review also focuses on some of the patents which are issued in the area of customer attrition or churn analysis. The goal of the research paper is to predict accurately the customer attrition rate in the Banking Sector. Objective: The main objective of this paper is to predict accurately the attrition rate in the Banking sector using an optimized deep feed-forward neural network. Methods: In the proposed work the predictive machine learning model is implemented using the optimized deep feed-forward neural network having five hidden layers in it. The model is trained using Adam optimizer algorithm to obtain the optimal accuracy. The Banking Churn data set is passed as input to the Optimized Deep Feed Forward Neural Network Model. In order to perform the comparative analysis, the same data set is passed as input to the other machine learning algorithm such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, and Artificial Neural Network. Results: The test results indicate that the proposed optimized deep feedforward neural Network model performed better in accuracy compared to existing machine learning techniques. Conclusion: The proposed optimized deep neural network model is an accurate model for customer attrition analysis in the Banking sector compared to the existing machine learning techniques.

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