Supervised Learning Technique for Prediction of Diseases

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)

Abstract

Lifestyle of a human being is changing day by day and leads human being to an unhealthy life. Apart from the routine exercise and healthy food, the health monitoring at a regular interval also becomes necessary to live long and healthy life. So, in this paper a system is proposed which will help in decreasing the progressive visits to the center moreover help in the early determination of risky sicknesses. This paper proposed a superior health monitoring framework utilizing neural network. In this, neural network (NN)-based health monitoring system is proposed as a solution to human health monitoring. In this, we are taking 276 instances and monitor their health by using 11 number of attributes. The dataset is got from UCI machine learning and from local specialist. All these attributes w.r.t patient are processed using NN in MATLAB and accordingly a result will be drafted. The previous techniques which were used to predict the heart, skin, liver disorder, diabetes, and cancer diseases are fuzzy logic, data mining, radial basis function network, recurrent network, etc. This paper presents our underlying attempt to grow such a framework with the assistance of NN by supervised learning method. In this proposed work, training part is 90% that means 248 instances used for training and rest 10% means 28 instances used for testing and validation. This system gives the more accurate result as compared to previous work, and it is the modified version of health monitoring system. This system shows an accuracy of 98.34% for training part which is the very good value for any data.

Keywords

Artificial neural network Health Multilayer perceptron Attributes 

Notes

Declaration

I Bharti Yadav hereby declare that half of the data used to do the testing of my above work is collected from a hospital (ethical committee) of live patient and another half is from a central repository on Internet. If any issue arises hereafter, then I will solely be responsible for this.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Chandigarh Group of CollegesLandranIndia

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