Prediction of kidney disease using machine learning algorithms

Abstract

The loss of renal function occurs gradually in diabetic kidney disease (DKD), which is associated with a high death rate. India is second only to China in the number of people living with DKD and it is expected that one million new cases arise in India each year. If diagnosed at an early stage, DKD may be effectively treated. DKD is more dangerous since it often has no early warning signs in its infancy. From a healthcare provider's standpoint, it is crucial to take preventative measures by using a machine-first model to foresee the beginning of DKD. The likelihood that a patient may acquire DKD can be estimated using their health records, and there are open source machine learning methods available to do this. The amount of clinical factors and the number of datasets used to train the algorithm both affect the prediction accuracy. A machine learning method and a booster algorithm were used in this work to increase the accuracy of DKD prediction. The strategy utilized in boosting algorithm produced more reliable outcomes than models used without boosting such as random tree, KNN and support vector machine.