A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

Autor:	Sathiya Narayanan, S Premanand
Rok vydání:	2021
Předmět:	History Computer science business.industry Tree based Artificial intelligence Machine learning computer.software_genre business computer Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 2115:012042
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/2115/1/012042
Popis:	The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::81624419519fa08b46606c33dd0c0fa7 https://doi.org/10.1088/1742-6596/2115/1/012042 Zobrazit plný text záznamu