Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method

• URL: http://arxiv.org/abs/2202.07779v1
• Date: Tue, 15 Feb 2022 23:06:49 GMT
• Title: Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method
• Authors: Nandan Kanvinde, Abhishek Gupta, Raunak Joshi
• Abstract summary: The dataset for Polycystic Ovary Syndrome is available, which is termed as an endocrinological disorder in women. Non-Parametric Supervised Ensemble machine learning methods can be used for prediction of the disorder in early stages.
• Score: 3.1871776847712523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical Research data used for prognostication deals with binary classification problems in most of the cases. The endocrinological disorders have data available and it can be leveraged using Machine Learning. The dataset for Polycystic Ovary Syndrome is available, which is termed as an endocrinological disorder in women. Non-Parametric Supervised Ensemble machine learning methods can be used for prediction of the disorder in early stages. In this paper we present the Bootstrap Aggregation Supervised Ensemble Non-parametric method for prognostication that competes state-of-the-art performance with accuracy of over 92% along with in depth analysis of the data.

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