Abstract

AbstractThe machine learning (ML) and Internet of things (IoT) technologies are increasingly focussed on decision tree classification algorithm. Its use is expanding through numerous fields, solving incredibly complex problems. DTCA is also being used in medical health data using computer-aided diagnosis to identify chronic kidney diseases like cancer and diabetes. Deep learning is a class of machine learning that utilizes neural networks to solve problems and learn unsupervised from unstructured or unlabelled data. The DL used to deep stacked auto-encoder and softmax classifier methods is applied for CKD. In this work, based an R Studio and Python Colab software using random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, CNN, RNN, MLP is used to predict multiple machine and deep learning techniques, discover an early diagnosis of CKD patients. In this work, classify the chronic kidney disease various stages are identified.KeywordsDecision tree classification algorithmComputational decision support systemChronic kidney diseaseDeep and machine learning algorithmsIoT and CKD stages

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call