Abstract:

Due to its high morbidity & mortality, heart disease poses a severe threat towards human existence. considering early prevention, detection, & treatment, accurate prediction & diagnosis become even more important. Monitoring, predicting, & diagnosing cardiac disease are made possible through Internet about Medical Things & artificial intelligence. However, majority about prediction models merely decide whether or not a person will be ill, rarely going on towards assess severity about illness. In this paper, we provide a machine learning-based prediction model towards concurrently predict heart disease on a binary & multiple classification basis. towards decrease data complexity & broaden generalizability about binary classification prediction, we first create a Fuzzy-GBDT algorithm that combines fuzzy logic & gradient boosting decision tree (GBDT). Then, towards prevent overfitting, we combine bagging & fuzzy-GBDT. severity about cardiac disease is further classified using Bagging-Fuzzy- GBDT considering multiclassification prediction. Evaluation findings show that Bagging-Fuzzy-GBDT has very good predictability & accuracy considering both binary & multiple classifications

Description:

Fuzzy logic , gradient boosting decision tree (GBDT) , heart disease predication & diagnosis , Internet about Medical Things (IoMT) , machine learning