Random Forest Tree Research Articles

Application of machine-learning model to optimize colonic adenoma detection in India.

There is limited data on the prevalence and risk factors of colonic adenoma from the Indian sub-continent. We aimed at developing a machine-learning model to optimize colonic adenoma detection in a prospective cohort. All consecutive adult patients undergoing diagnostic colonoscopy were enrolled between October 2020 and November 2022. Patients with a high risk of colonic adenoma were excluded. The predictive model was developed using the gradient-boosting machine (GBM)-learning method. The GBM model was optimized further by adjusting the learning rate and the number of trees and 10-fold cross-validation. Total 10,320 patients (mean age 45.18 ± 14.82years; 69% men) were included in the study. In the overall population, 1152 (11.2%) patients had at least one adenoma. In patients with age > 50years, hospital-based adenoma prevalence was 19.5% (808/4144). The area under the receiver operating curve (AUC) (SD) of the logistic regression model was 72.55% (4.91), while the AUCs for deep learning, decision tree, random forest and gradient-boosted tree model were 76.25% (4.22%), 65.95% (4.01%), 79.38% (4.91%) and 84.76% (2.86%), respectively. After model optimization and cross-validation, the AUC of the gradient-boosted tree model has increased to 92.2% (1.1%). Machine-learning models may predict colorectal adenoma more accurately than logistic regression. A machine-learning model may help optimize the use of colonoscopy to prevent colorectal cancers. ClinicalTrials.gov (ID: NCT04512729).

The Impact of Device Type Number on IoT Device Classification

Today, connected systems are widely used with the recent developments in technology. The internet-connected devices create data traffic when communicating with each other. These data may contain extremely confidential information. Observers can obtain confidential information from the traffic when the security of this traffic cannot be adequately ensured. This confidential information can be personal information as well as information about the type of device used by the person. Attackers could use machine learning to analyze encrypted data traffic patterns from IoT devices to infer sensitive information, even without decrypting the actual content. For example, if someone uses IoT devices for health monitoring or smoke detection, attackers could leverage machine learning to discern victims' habits or identify health conditions. An increase in the number of IoT devices may decrease the accuracy of classification when using machine learning. This paper presents the importance of the effect of device type number on the classification of IoT devices. Therefore, inference attacks on privacy with machine learning algorithms, attacks on machine learning models, and the padding method that is commonly used against such attacks are presented. Moreover, experiments are carried out by using the dataset of the traffic generated by the Internet of Things (IoT) devices. For this purpose, Random Forest, Decision Tree, and k-Nearest Neighbors (k-NN) classification algorithms are compared, and the accuracy rate changes according to the number of devices are presented. According to the results, the Random Forest and Decision Tree algorithms are found to be more effective than the k-NN algorithm. When considering a scenario with two device types, the Random Forest and Decision Tree algorithms achieved an accuracy rate of 98%, outperforming the k-NN algorithm, which had an accuracy rate of 95%.

Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages

Sphalerite is a commonly occurring mineral in natural systems and a prominent indicator mineral used in resource exploration. Its chemistry and associated mineral assemblages are both controlled by the physicochemical conditions of the local environment, such as temperature and sulfur fugacity. Accordingly, the chemistry of sphalerite and the nature of the associated minerals provide valuable clues about the classification of mineral deposits and their environment of formation. Nevertheless, exclusive reliance on the trace-element chemistry of sphalerite for deposit classification has its limitations given the multitude of factors (For example, deposit type, temperature, pressure, and background concentrations of elements) that affect its chemistry. To address this challenge, we develop machine learning models using the SHapley Additive exPlanations (SHAP) method to assess the importance of sphalerite trace-element chemistry and mineral assemblage information to distinguishing between five petrogenetically distinct mineral deposit types. This contribution demonstrates that a composite model that incorporates both data types markedly improve the accuracy of deposit type discrimination. The composite model is composed of two sub-models, Random Forest (RF) and Extra Random Trees (ERT), which are specifically employed for processing trace-element data and mineral assemblage data, respectively, due to their superior performance in handling these two distinct types of datasets. To simplify the end-user experience, we provide an executable file of the machine learning-based classifier, allowing it to be readily applied as an exploration tool using simple inputs. In summary, this work substantially enhances the confidence of Pb-Zn deposit type classification using sphalerite and introduces an innovative perspective on the application of machine learning to resolving complex geological problems.

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the gradient boosting regression tree model and the shapley additive explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties.

Predict the surface free energy of Ti/Zr/V chemical conversion coating on nickel by machine learning approach: Trees vs. neurons

The surface properties of chemical conversion treated materials have received increasing attention and surface free energy (SFE) is an important indicator of it. In this study, an environment-friendly Ti/Zr/V chemical conversion coating was deposited on the nickel and Artificial Neural Network (ANN), Random Forest (RF), and Extra Trees (ET) models were applied to further investigate the relationship between the SFE and the compositions of the conversion bath. The accuracy of SFE prediction of three machine learning models was quantified, and the obtained results reveal that the correlation coefficients for the ANN, RF, and ET models are 0.958, 0.979, and 0.963 for the prediction of dispersion components and 0.964, 0.942 and 0.921 for the prediction of polar components, respectively, and the ANN model shows more outstanding performance in general. The errors in the prediction of the ANN model for the polar and dispersion components of SFE are 0.336 and 0.421, respectively, which are smaller than the extended uncertainties obtained from conventional experimental methods. The obtained machine learning models can accurately predict the variations of SFE for modest changes in the conversion bath components, thus linking the formation mechanism of coating with the variations of SFE.

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach.

Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics.

Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones. We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes. Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16. CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.

Geospatial Assessment of Urban Sprawl: A Case Study of Herat City, Afghanistan

This study aims to investigate the spatial and temporal dynamics of urban sprawl in Herat City, Afghanistan, from 2000 to 2021 using GIS and remote sensing data (Landsat 7 and 8). In this study, three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART), were employed to classify the study area, and the accuracy of each algorithm for each study period was assessed. Based on the assessment results, the RF algorithm demonstrated higher accuracy and was selected as the classification algorithm. The Google Earth Engine cloud platform was utilized to classify the study area, and the GIS environment was employed for the creation of thematic layers. The analysis revealed a 30.06% increase in built-up areas from 2000 to 2021. Conversely, vegetation, water bodies, and bare land decreased by 8.51%, 1.08%, and 20.53%, respectively, during the same period. The findings indicated that Herat City experienced high-speed expansion between 2000 and 2013, while from 2013 to 2021; it developed at a medium speed. The Relative Shannon's entropy statistical algorithm was employed to quantify urban sprawl, and the results suggest a dispersed urban sprawl pattern. Internal migration to major cities due to conflicts, limited employment opportunities, and inadequate living amenities in rural areas has been a primary driver of urban sprawl in Herat City, Afghanistan.

Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms

Acoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this relationship is essential because low acoustic impedance values are closely associated with high porosity, facilitating the accumulation of more hydrocarbons. In this study, we estimate the acoustic impedance based on nine different inputs of seismic attributes in addition to depth and two-way travel time using three supervised machine learning models, namely extra tree regression (ETR), random forest regression, and a multilayer perceptron regression algorithm using the scikit-learn library. Our results show that the R2 of multilayer perceptron regression is 0.85, which is close to what has been reported in recent studies. However, the ETR method outperformed those reported in the literature in terms of the mean absolute error, mean squared error, and root-mean-squared error. The novelty of this study lies in achieving more accurate predictions of acoustic impedance for exploration.

EDT-STACK: A stacking ensemble-based decision trees algorithm for tire tread depth condition classification

Tires serve as the critical interface between a vehicle and the road. Their condition significantly impacts road safety, vehicle performance, fuel efficiency, and operational costs. Developing a machine learning model for accurately and efficiently classifying tire conditions using tread depth measurements poses a critical challenge in enhancing automotive safety and maintenance. This research aims to address this challenge by leveraging machine learning techniques to categorize and assess tire conditions based on tread depth, thereby contributing to developing a robust and automated system for tire safety condition monitoring in diverse vehicle fleets. This article presents a novel ensemble method-based machine learning algorithm called EDT-STACK (Stacking Ensembles-Based Decision Tree) that combines several decision tree-based methods using a stacking ensemble technique. The model is trained and tested on a dataset of 500 tires (new and disposed) with different tread depths, The model achieves 99 % accuracy in all classes, demonstrating its efficacy in classifying tire conditions from tread depth data. The study also compares the performance of EDT-STACK with other algorithms. Its superior performance outshines other decision tree algorithms, such as XGBoost, AdaBoost, Random Forest, ID3, and Histogram-based Decision Tree, which achieve accuracies of 98 %, 78 %, 85 %, 98 %, and 98 %, respectively. This study concludes that EDT-STACK offers an innovative solution for tire condition assessment, which can enhance road safety, reduce environmental impacts, and optimize vehicle maintenance.

Parents’ Mental Health Detection Having Children with Disabilities Using Machine Learning Technique

The journey of parents raising children with disabilities is fraught with unique challenges, underscoring the critical need for comprehensive research to illuminate statistical insights that can drive informed policy, bolster support systems, and inspire community initiatives. This study leverages a diverse array of sources, including national surveys, government databases, and peer-reviewed studies, to provide a nuanced understanding of the multifaceted experiences of these parents. Through meticulous data analysis spanning various dimensions, such as the prevalence of different disability types, socioeconomic disparities, geographic distribution, and access to essential services like healthcare and education, this research aims to offer a holistic perspective. Recognizing the pivotal role of machine learning, particularly in addressing mental health challenges, this study presents an innovative approach. Utilizing advanced techniques such as Qualitative Analysis, Regression Analysis, and logistic regression, the research lays the groundwork for a proposed system designed to predict and detect parental mental health issues within families raising disabled children. This proposed system integrates classification algorithms, leveraging both structured and unstructured data. For structured data, a combination of sophisticated algorithms including Random Forest, XGboost, KNN, SVM, and decision trees is employed. Meanwhile, Natural Language Processing (NLP) techniques are applied to unstructured data, enhancing the assessment of parental mental health.

Prediction of student exam performance using data mining classification algorithms

AbstractStudent outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students’ academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students’ academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

Data-driven approach for intelligent tunnel dust concentration prediction

The precise evaluation of tunnel dust concentration (TDC) stands as a primary concern within engineering practices. However, comprehensive and accurate prediction of TDC becomes increasingly challenging due to the expanding multi-source datasets and cumulative errors. This study employs innovative data-driven ensemble learning methodologies, specifically random forest (RF) and gradient boosting regression tree (GBRT), to delineate the intricate relationships between TDC values and various rock tunnel characteristics. Five diverse variables sourced from multiple origins are examined and employed as inputs within the database, while the TDC values derived from the dust concentration meter serve as the target outputs. Subsequently, a Bayesian optimization approach, the Tree-structured Parzen Estimator (TPE), is introduced to automatically ascertain the optimal hyper-parameters for the ensemble models. A comprehensive comparison is conducted between the two ensemble learning models and a singular machine learning algorithm, the classification and regression tree (CART), concerning predictive accuracy and resilience via 10-fold cross-validation (CV). The findings reveal the superior performance of the hybrid ensemble learning models over the individual ML models. Notably, the TPE-GBRT algorithm adeptly captures the measurement evolution, showcasing the lowest prediction errors and the highest correlation coefficient. These predicted outcomes significantly contribute to enhancing the engineering comprehension of the interrelation between rock tunnel parameters and TDC values.

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Abstract Wetlands are an integral part of our global ecosystems and play crucial roles in ecological functions such as carbon sequestration, flood mitigation, water purification, and recreational activities. The Ramsar Convention is the most significant wetland protection pact and is doing tremendous work in conserving wetlands worldwide. However, the wetlands area is still under threat due to anthropogenic activity. The current study utilized drone images, chlorophyll measurements and machine leaning to discriminate and map vegetation at marsh wetland area—the Ramsar site. The high‐resolution, multispectral imagery is acquired using a drone‐mounted MICAsense sensor. Eight spectral indices such as Normalized Difference Water Index (NDWI), Two‐Band Algorithm (2BDA), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Vegetation Index (NDVI) Enhanced Normalized Difference Vegetation Index (ENDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalised Difference RedEdge (NDRE) were calculated on the acquired imagery in order to discriminate the different vegetation covers such as floating aquatic vegetation (FAV), open water, and other vegetations types. These include the following: Eichhornia, Nymphea, Oleracea, Paspalam, and Oryza from agriculture land at the study site. Two models (viz., the Taylor plot and the Lek Profile methods) were employed to assess the sensitivity of the spectral indices for prediction of chlorophyll and vegetation discrimination. It is inferred from both methods that NDCI was most sensitive for chlorophyll prediction of vegetation followed by NGRDI/ ENDVI/ 2BDA and NDVI for chlorophyll prediction in wetland ecosystems. Further, three machine learning algorithms, support vector machine (SVM), random forest (RF), and gradient tree boost (GTB), were utilized for classification, and the performance accuracy of GTB was found to be the highest (0.893), followed by RF (0.851) and SVM (0.723). The GTB algorithm was applied over NDCI for vegetation discrimination. The study revealed that Eichhronia sp. is abundantly present at the study site; hence, strategic management plans should be carried out for the eradication of invasive species and proper management of wetland vegetation.

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

AI Based Identification of Inappropriate Language

Abstract: In the current digital era, the widespread use of online communication has raised the demand for automated systems that can recognize and block hate speech, improper language, and objectionable information. The usefulness of three different machine learning algorithms—Decision Trees, Random Forest, Long Short-Term Memory (LSTM), and others—in determining if a given text contains objectionable language is investigated in this work. While LSTM and represent state-of-the-art deep learning algorithms capable of processing unstructured text input, Random Forest and Decision Trees are standard machine learning techniques that rely on organized feature engineering. This study compares different algorithms in an effort to shed light on their advantages and disadvantages in dealing with the changing demands of online content moderation. Our results show each model's accuracy, precision, recall, and F1-score.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

AbstractTo solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

CONTEXTAccurate estimating of crop yield is crucial for developing effective global food security strategies which can lead to reduce of hunger and more sustainable development. However, predicting crop yields is a complex task as it requires frequent monitoring of many weather and socio-economic factors over an extended period. Satellite remote sensing products have become a reliable source for climate-based variables. They are easier to obtain and provide detailed spatial and temporal coverage. OBJECTIVEThe aim of this study is to assess the effectiveness of implement a novel optimization algorithm, called Randomized Search cross validation (RScv), on various machine learning algorithms and measure the prediction accuracy enhancement. METHODSAnnual yields of four crops (Barley, Oats, Rye, and Wheat) were predicted across 20 European countries for 20 years (2000–2019). Two NASA missions, namely GPCP and GLDAS satellites, provided us with climate- and soil-based input variables. Those variables were employed as the input of four ensemble Machine Learning (ML) algorithms (Ada-Boost (AB), Gradient Boost (GB), Random Forest (RF) and Extra Tree (ET)) which are faster and more adoptable compare to classic AI algorithms. RESULTS AND CONCLUSIONSMain results show that applying RScv improves the prediction ability of all ML models over the four crops. In particular, the RScv-AB reaches the overall highest accuracy for predicting yields (Rmax2=0.9). Spatial evaluation of predicting errors depicts that the proposed models were more shifted toward underestimation. An uncertainty analysis was also carried out which shows that applying ML algorithms creates higher and lowers uncertainty in Barley and Wheat respectively. SIGNIFICANCEConsidering the robustness of the optimised ML models and the global coverage of remote sensing data, our current methodology demonstrates great transferability and can be applied in other regions across the globe with higher temporal extents. In addition, this tool could be beneficial to decision makers in various sectors to improve the water allocations, deal with climate change effects and keep sustainable agricultural development.

Performance of Random Oversampling, Random Undersampling, and SMOTE-NC Methods in Handling Imbalanced Class in Classification Models

One common challenge in classification modeling is the existence of imbalanced classes within the data. If the analysis continues with imbalanced classes, it is probable that the result will demonstrate inadequate performance when forecasting new data. Various approaches exist to rectify this class imbalance issue, such as random oversampling, random undersampling, and the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC). Each of these methods encompasses distinct techniques aimed at achieving balanced class distribution within the dataset. Comparison of classification performance on imbalanced classes handled by these three methods has never been carried out in previous research. Therefore, this study undertakes an evaluation of classification models (specifically Gradient Boosting, Random Forest, and Extremely Randomized Trees) in the context of imbalanced class data. The results of this research show that the random undersampling method used to balance the class distribution has the best performance on two classification models (Random Forest and Gradient Boosted Tree).

Comparison and Analysis of the Effectiveness of Linear Regression, Decision Tree, and Random Forest Models for Health Insurance Premium Forecasting

Health insurance is a type of insurance that covers individual and family medical expenses and is important for the health and financial security of individuals and families. To better predict the demand for health insurance, three regression models in machine learning - random forest, linear regression, and decision tree - are widely used for health insurance prediction. Among these three regression models, random forest regression has the best prediction effect with a model score of 0.8564, which is the best prediction effect among the three models. Random forest regression is an integrated learning method that combines multiple decision tree models into a more powerful model that can effectively avoid overfitting problems and can handle large amounts of data. Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584. The linear regression model is a basic regression model that can be used to predict a linear relationship between two variables. In health insurance prediction, linear regression modeling can be used to predict the linear relationship between health insurance costs and related factors such as age, gender, and illness. The worst predictor was the decision tree, with a model score of 0.7097. The decision tree model can be used in Medicare forecasting to predict nonlinear relationships between Medicare costs and related factors such as age, gender, and illness.