Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.

Energy recovery efficiency and cost analysis of VOC thermal oxidation pollution control technology.

Thermal oxidation of VOC is extremely energy intensive, and necessitates high efficiency heat recovery from the exhaust heat. In this paper, two independent parameters heat recovery factor (HRF) and equipment cost factor (ECF) are introduced. HRF and ECF can be used to evaluate separately the merits of energy efficiency and cost effectiveness of VOC oxidation systems. Another parameter equipment cost against heat recovery (ECHR) which is a function of HRF and ECF is introduced to evaluate the merit of different systems for the thermal oxidation of VOC. Respective cost models were derived for recuperative thermal oxidizer (TO) and regenerative thermal oxidizer (RTO). Application examples are presented to show the use and the importance of these parameters. An application examples show that TO has a lower ECF while RTO has a higher HRF. However when analyzed using ECHR, RTO would be of advantage economically in longer periods of use. The analytical models presented can be applied in similar environmental protection systems.