BACKGROUND Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medic...

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuris...

In recent years, deep learning credit scoring models have become a hot research topic in Internet finance. However, most of the existing studies are based on neural network models, whose structure is difficult to design. Moreover, previous seldom considers impact class imbalance problems performance. To fill this gap, we propose new model forest (DF) and resampling methods. First, combine DF wi...

Abstract Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining customers. CCP telecommunication sector has become essential need owing to rise number of service providers. Recently, machine learning (ML) deep (DL) models have begun develop effective model. This paper presents n...

This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and C...

Heart disease has become one of the most prevailing universal diseases in world today. It is estimated that 32% all deaths worldwide are caused due to heart diseases. One major causes for this its extremely difficult even medical practitioners predict as attacks it a complex task which requires great amount knowledge and experience. The number by hugely increased recent past. Machine learning p...

A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more i...

Soybean disease has become one of vital factors restricting the sustainable development high-yield and high-quality soybean industry. A hybrid artificial neural network (ANN) model optimized via particle swarm optimization (PSO) algorithm, which is denoted as PSO-ANN, proposed in this paper for diseases identification based on categorical feature inputs. Augmentation dataset created Synthetic m...

The application of machine learning techniques in agriculture, particularly harvest forecasting, is gaining traction as a means addressing this issue. major project, "Optimizing Crop Yields through Machine Learning-Based Prediction," takes comprehensive approach to issue by considering variety parameters, including temperature, humidity, rainfall, and soil nutrient levels, Figure out which crop...