Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This paper presents the development of Artificial Neural Network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models were finished, the focus was to decrease the Mean Absolute Percentage Error by adjusting the parameters such as epoch, learning rate and neuron in different layers. Both Feed Forward and Recurrent Neural Networks were implemented to forecast the yearly claims amount. In conclusion, the Artificial Neural Network Model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims. Recurrent neural network outperformed Feed Forward neural network in terms of accuracy and computation power required to carry out the forecasting.

Health Insurance Claim Prediction Using Artificial Neural Networks

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

An Efficient Feed Foreword Network Model with Sine Cosine Algorithm for Breast Cancer Classification

This article describes how breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. The diagnoses of breast cancer include mammograms, breast ultrasound, magnetic resonance imaging (MRI), ductogram and biopsy. Biopsy is best and only way to know if the breast tumour is cancerous. Reports say that positive detection of breast cancer through biopsy can reach as low as 10%. So many statistical techniques and cognitive science approaches like artificial intelligence are being used to detect the type of breast cancer in a patient. This article presents the breast cancer classification using a feed foreword neural network trained by a sine-cosine algorithm. The superiority of the SCA-NN is shown by experimenting on the Wisconsin Hospital data set and comparing with the recently reported results. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

Position and Tilt Control of Two-Wheeled Robot (TWR)

This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot (TWR) system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference system(ANFIS) controller. A Matlab Simulink model of the system has been built using Newton's second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.