Elemental hydrochemistry assessment on its variation and quality status in Langat River, Western Peninsular Malaysia.

This paper discusses the hydrochemistry variation and its quality status in Langat River, based on the chemistry of major ions, metal concentrations and suitability for drinking purposes. Water samples were collected from 30 different stations to assess their hydrochemical characteristics. The physico-chemical parameters selected were temperature, electrical conductivity, total dissolved solids (TDS), salinity, dissolved oxygen , pH, redox potential, HCO3, Cl, SO4, NO3, Ca, Na, K, Mg, 27Al, 138Ba, 9Be, 111Cd, 59Co, 63Cu, 52Cr, 57Fe, 55Mn, 60Ni, 208Pb, 80Se and 66Zn to investigate the variation of the constituents in the river water. Most of the parameters comply with the Drinking Water Quality Standard of the World Health Organization and the Malaysian National Standard for Drinking Water Quality by the Malaysia Ministry of Health except for EC, TDS, Cl, HCO3, SO4, Na, Mg, Al, Fe and Se. The results show that the Langat River is unsuitable for drinking purposes directly without treatment

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set

An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization

Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier