Heart disease prediction using machine learning techniques: a quantitative review
Heart diseases or the cardiovascular diseases are the main reasons for a large number of
deaths in the world today. Heart disease affects the functioning of blood vessels and can …
deaths in the world today. Heart disease affects the functioning of blood vessels and can …
Disease detection and prediction using the liver function test data: A review of machine learning algorithms
In the last decade, there has been an admirable improvement in the classification accuracy
of various machine learning techniques used for disease diagnosis. This even aids in …
of various machine learning techniques used for disease diagnosis. This even aids in …
[PDF][PDF] Numerical and experimental investigation of meteorological data using adaptive linear M5 model tree for the prediction of rainfall
Real-time predictions are always important for accurate and systematic thinking in planning
future processes. The failure in the availability of current machine learning approaches is a …
future processes. The failure in the availability of current machine learning approaches is a …
Bio-hydrogen production from the photocatalytic conversion of wastewater: Parametric analysis and data-driven modelling using nonlinear autoregressive with …
The quest for energy and environmental sustainability necessitates an increasing interest in
the photocatalytic conversion of wastewater to biohydrogen. However, the complexity of the …
the photocatalytic conversion of wastewater to biohydrogen. However, the complexity of the …
A pragmatic comparison of supervised machine learning classifiers for disease diagnosis
This study focuses on comparing the different supervised machine learning classifiers such
as Logistic Regression, Naïve Bayes, Support Vector Machine, K-Nearest Neighbour …
as Logistic Regression, Naïve Bayes, Support Vector Machine, K-Nearest Neighbour …
[PDF][PDF] An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data
Traditional and ensemble methods are linear models which are considered the most
popular techniques for various learning tasks for the prediction of both nominal and …
popular techniques for various learning tasks for the prediction of both nominal and …
Knowledge discovery in geographical sciences—A systematic survey of various machine learning algorithms for rainfall prediction
One of the biggest challenges faced by humanity over time is weather prediction. Rainfall
prediction plays a critical role in agricultural sciences, besides it is pivotal in the prediction of …
prediction plays a critical role in agricultural sciences, besides it is pivotal in the prediction of …
To ameliorate classification accuracy using ensemble distributed decision tree (DDT) vote approach: An empirical discourse of geographical data mining
Weather data of Kashmir province has 6 attributes recorded at three different substations.
This paper proposes a distributed decision tree algorithm and its implementation on …
This paper proposes a distributed decision tree algorithm and its implementation on …
[PDF][PDF] A hybrid adaptive grey wolf Levenberg-Marquardt (GWLM) and nonlinear autoregressive with exogenous input (NARX) neural network model for the prediction …
Rainfall prediction, a type of weather forecasting, has a big impact on agriculture and
farming, as well as other industries like natural disaster management. One of the most …
farming, as well as other industries like natural disaster management. One of the most …
Performance evaluation of GINI index and information gain criteria on geographical data: An empirical study based on JAVA and Python
In this paper, generation and performance comparison is established between information
gain and GINI index on raw geographical dataset. Concrete results are drawn, and …
gain and GINI index on raw geographical dataset. Concrete results are drawn, and …