Hyperparameter Optimization of Random Forest Algorithm Technique in Acute Coronary Syndrome Cases

The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘Bayesian Weighted Random Forest’(B-RF), focus on the problem that inaccurate decision tree classification caused by the same voting weights in the traditional random forest model. The main idea underlying the proposed model is to replace the supermajority voting of random forests into weighted voting, fully consider the difference of classification ability of each decision tree, using the Bayesian formula to dynamically update the weight value for each tree, so that the strong classifier has higher voting power and effectively improves the overall performance of classification. Through the verification of UCI database, the results show that the classification accuracy of the proposed weighted random forest model is higher. This illustrate the outperformance of the proposed model in comparison with the general random forest algorithm.

In the midst of climate change, the need for accurate predictions of dam inflow to reduce flood damage along with stable water supply from water resources is increasing. In this study, the process and method of selecting the optimal deep learning model using hydrologic data over the past 20 years to predict dam inflow were shown. The study area is Andong Dam and Imha Dam located upstream of the Nakdong River in South Korea. In order to select the optimal model for predicting the inflow of two dams, sixteen scenarios (2 × 2 × 4) are generated considering two dams, two climatic conditions, and four deep learning models. During the drought period, the RNN for Andong Dam and the LSTM for Imha Dam were selected as the optimal models for each dam, and the difference between observations was the smallest at 4% and 2%, respectively. In typhoon conditions, the GRU for Andong Dam and the RNN for Imha Dam were selected as optimal models. In the case of Typhoon Maemi, the GRU and the RNN showed a difference of 2% and 6% from the observed maximum inflow, respectively. The optimal recurrent neural network-based models selected in this study showed a closer prediction to the observed inflow than the SFM, which is currently used to predict the inflow of both dams. For the two dams, different optimal models were selected according to watershed characteristics and rainfall under drought and typhoon conditions. In addition, most of the deep learning models were more accurate than the SFM under various typhoon conditions, but the SFM showed better results under certain conditions. Therefore, for efficient dam operation and management, it is necessary to make a rational decision by comparing the inflow predictions of the SFM and deep learning models.

Develop and evaluate a machine learning (ML) model based on CT radiomics for the identification of non-tuberculous mycobacterial lung disease (NTM-LD) and pulmonary tuberculosis (PTB). Retrospectively, chest CT images with NTM-LD and PTB patients confirmed at Medical Center 1 between January 2019 to December 2024 were collected. The dataset was divided into a training cohort and a validation cohort in a 7:3 ratio. Additionally, patients from medical center 2 were collected for external test. A radiomics model was constructed using five machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Quadratic Discriminant Analysis (QDA), YeoJohnson_LR, and YeoJohnson_LR (LDA). Receiver operating characteristic (ROC) and area under the curve (AUC) were used to evaluate the diagnostic efficacy of the five models, and the optimal prediction model was obtained. The optimal model was compared with three radiologists in the testing cohort. A total of 1,512 cases were included, including 1,407 cases from Center 1 (NTM-LD: 547; PTB: 860) and 105 patients from Center 2 (NTM-LD: 32; PTB: 73). Patients in the NTM-LD group were significantly older than those in the PTB group (p < 0.001). There was a significant gender difference between the NTM-LD group and the PTB group (p = 0.005). By comparing the five models, it was found that the YeoJohnson_LR (LDA) model was the best-performing prediction model, with an accuracy of 0.8286 in the external test. The AUCs of the YeoJohnson_LR (LDA) model on the training, validation, and test cohort were 0.8421, 0.8037, and 0.8233, respectively. In comparison with radiologists, the YeoJohnson_LR (LDA) model demonstrated gains of 3.12 ~ 15.62% in sensitivity and 6.85 ~ 12.33% in specificity. The YeoJohnson_LR (LDA) model can be used to distinguish NTM-LD from PTB, assisting in rapid clinical diagnosis and benefiting patients with NTM-LD.

To develop an integrated model that combines multimodal fundus image features for accurately predicting the individualized risk of progression from early to late stages of age-related macular degeneration (AMD). A retrospective analysis was conducted on the data of 324 patients with AMD. The patients were randomly divided into a training set (n = 227) and a validation set (n = 97) at a ratio of 7:3. The follow-up period was 3 years, and patients with disease progression were defined as the progression group. In the training set, indicators related to prognosis were screened through univariate analysis. After variable compression by LASSO regression, independent influencing factors for poor prognosis were determined using multivariate logistic regression. Random Forest, Support Vector Machine, XG BOOST, and K-Nearest Neighbor algorithm prediction models were constructed using Python software. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC), and the optimal model was selected. There were no significant differences in the baseline characteristics between the training set and the validation set patients (P > 0.05), indicating comparability. In the training set, multivariate logistic regression analysis showed that pigmentary abnormalities, the total area of drusen in the macular area, and the ellipsoid zone were independent risk factors for disease progression (P < 0.05), while subfoveal choroidal thickness and choroidal capillary blood flow density were independent protective factors (P < 0.05). The AUC values of the Random Forest model (0.779 in the training set and 0.700 in the validation set) were significantly higher than those of the K-Nearest Neighbor algorithm (0.717 in the training set and 0.596 in the validation set), the Support Vector Machine model (0.768 in the training set and 0.646 in the validation set), and XG BOOST (0.702 in the training set and 0.762 in the validation set), making it the optimal prediction model. In this study, an AMD progression prediction model based on multimodal fundus images was successfully developed, which can effectively identify patients at high risk of progression and provide a new paradigm for clinical individualized precision medicine.

The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.

Skin disease is a very vulnerable and severe issue in today's world. Skin disorder classification is crucial for diagnosis. Several new data mining algorithms have been developed to classify and interpret medical images. The functionality of the K-Nearest Neighbors (KNN) Random Forest (RF) Algorithm is described in this article, along with an analysis of its results. Furthermore, this study demonstrates a high-performing approach that saves both effort and money. The proposed model is designed based on KNN and the Random Forest algorithm. The patient can use this model to classify his skin disease as a primary detection, and the doctor also can ensure his judgment by using this proposed model. Traditional skin disease diagnosis is an expensive and time-consuming procedure. This paper's proposed classification model will identify ten different skin diseases. The Random Forest algorithm has a testing accuracy of 94.22 percent, and K-Nearest Neighbors (KNN) has a testing accuracy of 95.23 percent. The KNN algorithm has an F1 Score of 95.98 percent, whereas the Random Forest (RF) algorithm has an F1 Score of 95.94 percent. It can be increased by expanding the dataset and more feature extraction. This approach may benefit individuals with skin illness who are looking to save money and time as well as avoid skin cancer by identifying cancer at an early stage.

Hydrological simulations and predictions are vital aspects of hydrological change research. Accurate predictions of hydrological factors such as stage and discharge are essential for water resources planning, reservoir dispatching and operation, shipping management and flood control. River discharge forecasting during flood seasons is an important issue in water resources planning and management. To improve the calibration accuracy and stability of the stage–discharge relationship model, the feasibility of integrated algorithms for studying the stage–discharge relationship was explored. A random forest (RF) algorithm based on a neural network (NN) was developed using a framework of integrated algorithms. First, the Levenberg–Marquardt (LM) algorithm was used to optimise the weight updating process of a back-propagation (BP) NN and improve the convergence speed. Then, the LM-BP algorithm was used as a decision tree to build an RF algorithm. The model was tested using hydrological data from Hongqi station on the Dadu River in China in the flood season. Results for the classical model, BP NN model, LM-BP NN model and optimised algorithm model were evaluated based on the mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The performance indicators showed that the optimised algorithm model (MAE = 3.13 m3/s, MSE = 19.28 m3/s and MAPE = 1.8%) was superior to the other models and showed high accuracy and good stability in flood-season flow forecasting.

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry.

Investigating Rare Events and a Discussion on Waiting Room Chest Pain Evaluations

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

Application of random forest algorithm in hail forecasting over Shandong Peninsula

Context.As an increasing number of spectroscopic surveys become available, an automated approach to spectral classification becomes necessary. Due to the significance of the massive stars, it is of great importance to identify the phenomenological parameters of these stars (e.g., the spectral type), which can be used as proxies to their physical parameters (e.g., mass and temperature).Aims.In this work, we aim to use the random forest (RF) algorithm to develop a tool for the automated spectral classification of OB-type stars according to their sub-types.Methods.We used the regular RF algorithm, the probabilistic RF, which is an extension of RF that incorporates uncertainties, and we introduced the KDE – RF method which is a combination of the kernel-density estimation and the RF algorithm. We trained the algorithms on the equivalent width (EW) of characteristic absorption lines measured in high-quality spectra (signal-to-noise (S/N)≳50) from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral types and luminosity classes. By following an adaptive binning approach, we grouped the labels of these data in 11 spectral classes within the O2-B9 range. We examined which of the characteristic spectral lines (features) are more important for the classification based on a number of feature selection methods, and we searched for the optimal hyperparameters of the classifiers to achieve the best performance.Results.From the feature-screening process, we find that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We find that the overall accuracy score is ∼70%, with similar results across all approaches. We apply our model in other observational data sets providing examples of the potential application of our classifier to real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries, especially for data of similar quality to the training sample. In addition, we propose a reduced ten-features scheme that can be applied to large data sets with lowerS/N ∼ 20 − 50.Conclusions.The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multiclass classification problems (i.e., 11 classes), (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work is applicable to products from different surveys in terms of quality (e.g., different resolution) and different formats (e.g., absolute or normalized flux), while our classifier is agnostic to the luminosity class of a star, and, as much as possible, it is metallicity independent.

This research aims to predict the Heath care Insurance Premi by analyzing the customer characteristics Random Forest Algorithm. Secondly, this research will determine feature importance from customer characteristics that drive the closing decision. This research methodology is quantitative, and data mining methodology and data will be derived from primary data, and 202 data will be processed and cleaned into 148 data. This research also uses supervisory learning and using the random forest algorithm. The novelty of this research, Firstly, the health care insurance premium can be predicted by the machine learning random forest algorithm. Secondly, the sequence of importance from the customer characteristics can be determined by the feature of importance function at the random forest. A random forest algorithm can predict the prospect data test with an accurate value of 63,33 %. First, the crucial question that has to be made is how much the income is; second is age, is between 45-55 years old, and the third is how many dependent. The decision tree graph helps construct the questioner's level, leading to a monthly target premium of 3 million to 5 million. Keywords: Health care Insurance Premi Prediction, Random forest Algorithm, The feature Importance.

Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

Text Classification is vital and challenging due to varied kinds of data generated these days; emotions classification represented in form of text is more challenging due to diverse kind of emotional content and such content is growing on web these days. This research work is classifying emotions written in Hindi in form of poem with 4 categories namely Karuna, Shanta, Shringar and Veera. POS tagging is used on all the poem and then features are extracted by observing certain poetic features, two types of features are extracted and the results in terms of accuracy is measured to test the model. 180 Poetries were tagged and features were extracted with 8 different keywords, and 7 different keywords. The model is build with Random Forest, SGDClassifier and was trained with 134 poetries and tested with 46 Poetries for both types of features. The results with 7 keyword feature is comparatively better than 8 keyword feature by 7.27% for Random Forest and 10% better for SGDClassifier. Various combinations of hyper parameters are used to get the best results for statistical measure precision and recall for performance tuning of the model. The model is also tested with k – fold cross validation with average result 62.53% for 4 folds and 60.45% for 8 folds with Random Forest and 54.42% for 4 folds and 48.28% for 8 folds with SGDClassifier, the experimentation result of Random Forest is better than SGDClassifier on the given dataset.