Early Stage Prediction of Chronic Kidney Disease with Novelty on Machine Learning Algorithms
What is it about?
Chronic Kidney Disease (CKD) is a critical and potentially life-threatening condition due to the impairment of kidney function and renal disorders. Early detection of CKD from the biological parameters would save people from this crisis. Machine Learning algorithms are playing a predominant role in disease diagnosis and prognosis. This work generates compound features from CKD indicators by two novel algorithms: Correlation-based Weighted Compound Feature (CWCF) and Feature Significance based Weighted Compound Feature (FSWCF).
Why is it important?
The features generated by these algorithms are validated on different machine learning algorithms as a test for generality. Various metrics like prediction accuracy gives superior results compared to multiple other approaches. The accuracy of CWCF over different methods like LR is 97.23%, Gaussian NB is 99%, SVM is 99.18%, and RF is 99.89%, which is substantially higher than the approaches without proper methods feature analysis. The results suggest that generated compound features improve the predictive power of the algorithms.