Support Vector Machine-based Algorithm Research Articles

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

Quantum squirrel search algorithm based support vector machine algorithm for brain tumor classification

AbstractA Brain tumor is growth or mass of irregular cells inside your brain, several various kinds of brain tumors survive. A few brain tumors are cancerous (malignant), also various brain tumors are noncancerous (benign). The existing approach faces problem related to local optima issues, complexity in computational time, less convergence speed and less exploration ability. The stimulated quality Selection of Quantum Squirrel Search Algorithm (QSSA) is based on equally appearance with methylation information of prostate cancer. Issues with multiple models, multiple dimensions, and unimodal optimization are all addressed by this QSSA concept. The input image of the CE‐MRI dataset consists of 3064 segments with comprise (708 slices) meningiomas, (1426 slices) gliomas and (930 slices) pituitary tumors. In order to extract appropriate data from an image, a convolutional neural network (CNN) executes a number of mathematical processes, including convolutions and pooling. The CNN model's benefits include a large number of important features that can be extracted and good accuracy. Then, Support Vector Machine (SVM), a machine learning technique used for supervised learning, is typically associated within the double classification. The SVM model benefits from a large effective dimensional space and adequate memory. The proposed QSSA has obtained high Accuracy 98.3%, Sensitivity 95.4% and Specificity 97.9% than existing Correlation Learning Mechanism (CLM) which has 90.4% accuracy, 86% sensitivity and 91.5% specificity respectively.

A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.

This study aimed to develop an algorithm to distinguish the patients with bisphosphonate-related osteonecrosis of the jaws (BRONJ) from healthy controls using CBCT images by evaluating both trabecular and cortical bone changes through the whole body of the mandibular bone. Patient data set was created from axial CBCT images of 7 BRONJ patients (28 slices) and 8 healthy controls (27 slices). The healthy bone of healthy controls, bone sclerosis of BRONJ patients, bone necrosis of BRONJ patients, and normal appearing bone of BRONJ patients (NBP) were labeled on CBCT images by three maxillofacial radiologists. Proposed algorithm had preparation and background cancellation, mandibular bone segmentation and centerline determination, spatial transformation of gray values, and classification steps. Significant differences between the statistical moments (mean, variance, skewness, kurtosis, standard error, median, mode and coefficient of variance) of healthy and diseased (bone sclerosis and necrosis) groups were observed (p = 0.000, p < 0.05). Also, variations were noted between healthy controls and NBP of BRONJ patients (p = 0.000, p < 0.05).The statistical moments were utilized to develop the algorithm which has resulted with accuracy of 0.999, sensitivity of 0.998, specificity of 0.998, precision of 1, recall of 0.998, AUC of 1, and F1 score of 0.999 in identification of BRONJ patients from healthy ones. The proposed algorithm differentiated the mandibular bones of the healthy and the BRONJ patients with high accuracy in the present test sample.

Conceptual Framework for Dynamic Optimal Airspace Configuration for Urban Air Mobility

In this work, a framework for optimizing the configuration of service areas in airspace into disparate partitions is demonstrated in the context of urban air mobility (UAM) operations. This framework is applied to a conceptual UAM airspace configuration, where a free-flight-based routing service and a corridor-based routing service are dynamically allocated to control different portions of the airspace over time, based on traffic demand. This allocation seeks to determine the least amount of structured coordination (in terms of active flight corridors) needed to safely meet traffic demand. This framework integrates several modeling components, including a novel spatiotemporal graph theoretic UAM traffic model capable of optimizing vehicle trajectories while maintaining multiple flight constraints. Airspace complexity and trajectory efficiency metrics are both implemented to quantify the overall safety and cumulative cost of routing a set of missions according to a given airspace configuration. Finally, spatial airspace partitions are managed using a support vector machine-based algorithm. Metrics are then applied to optimize the airspace configurations, according to desired objectives. Simulated results show that this framework can produce airspace configurations that ensure safety, while providing trajectory efficiency more effectively than purely uniform free-flight or corridor-based flight. This is demonstrated for both low- and high-density traffic scenarios.

Refined Composite Multiscale Fluctuation Dispersion Entropy and Supervised Manifold Mapping for Planetary Gearbox Fault Diagnosis

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from planetary gearbox signals under multiple scales. Subsequently, as a supervised manifold mapping method, supervised isometric mapping (S-Iso) was applied to decrease the dimensions of the original features and remove redundant information. Lastly, the marine predator algorithm-based support vector machine (MPA-SVM) classifier was employed to achieve intelligent fault diagnosis of planetary gearboxes. The suggested RCMFDE combines the composite coarse-grained construction and refined computing technology, overcoming unstable and invalid entropy in the traditional multiscale fluctuation dispersion entropy. Simulation experiments and fault diagnosis experiments from a real planetary gearbox drive system show that the complexity measure capability and feature extraction effectiveness of the proposed RCMFDE outperform the multiscale fluctuation dispersion entropy (MFDE) and multi-scale permutation entropy (MPE). The S-Iso’s visualization results and dimensionality reduction performance are better than principal components analysis (PCA), linear discriminant analysis (LDA), and isometric mapping (Isomap). Moreover, the suggested fault diagnosis scheme has an accuracy rate of 100% in identifying bearing and gear defects in planetary gearboxes.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

Support vector machine based disease classification model employing hasten eagle Cuculidae search optimization

SummaryThe enormous growth in the field of medicine utilizes machine learning for the well‐being and robustness of human beings. The emerging rate of the disease due to urbanization or various other global facts increases the mortality rate of the individual every year. The detection and classification of disease at the proper time helps an individual to be alive and healthy. Various methods of machine learning are accomplished to classify the diseases, but there is a necessity for the enhancement in the classification methods. In this research, the disease classification is performed using the proposed hasten eagle Cuculidae search optimization algorithm based support vector machine classifier (HECSO‐SVM) with the hybrid kernel function. The feature extraction with the statistical and correlation features presents the effective feature vector for classification and the curse of dimensionality is handled using the proposed HECSO algorithm. Initially, the data will be collected and preprocessing is performed and the features are extracted and selected using the optimization techniques. The proposed method effectively reduces the computation time and increases the accuracy of classification. The analysis is made based on the four different datasets such as Parkinson, heart, lung, and hepatitis that obtains an accuracy rate of 94.582%, 94.100%, 93.985%, 94.582%.

Automatic prediction of coronary artery disease using differential evolution-based support vector machine

Coronary artery disease (CAD) is a common heart disease that causes the blockage of coronary arteries. To reduce fatality, an accurate diagnosis of this disease is very important. Angiography is one of the most trustworthy and conventional methods for CAD diagnosis however, it is risky, expensive, and time-consuming. Therefore in this study, we proposed a differential evolution-based support vector machine (SVM) for early and accurate detection of CAD. To improve the accuracy, different data preprocessing techniques such as one-hot encoding and normalization are also used with differential evolution for feature selection before performing classification. The proposed approach is benchmarked with the Z-Alizadeh Sani and Cleveland datasets against four state-of-the-art machine learning algorithms, and a highly cited genetic algorithm-based SVM (N2GC-nuSVM). The experimental results show that our proposed differential evolution-based SVM outperforms all the compared algorithms. The proposed method provides accuracies of 95±1% and 86.22% for predicting CAD on the benchmark datasets.

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.

Lead Zirconate Titanate-based bolt looseness monitoring using multiscale singular spectrum entropy analysis and genetic algorithm-based support vector machine

The looseness monitoring of bolted joints is a significant issue to ensure structural integrity and safety in the industrial field. This paper proposes a novel approach to monitor bolt looseness based on piezoelectric active sensing. During the research, piezoelectric material is acted as an exciter to generate ultrasonic signals and a transducer is used to receive ultrasonic signals. In the process of signal processing, singular spectrum analysis (SSA) including phase reconstruction and principal component analysis is adopted to decompose the signal. Multiscale sample entropy (MSE) is employed to map the dynamic characteristics and regularity of the decomposed signals on multiple scales. The proposed strategy, named multiscale singular spectrum entropy analysis, refers to use MSE values of the new time series decomposed and reconstructed by SSA, to extract signal characteristics. Such a strategy can explore the underlying dynamical characteristics of a signal quantitatively in the reconstructed phase space. In our research work, SSA is employed to decompose the signals acquired by Lead Zirconate Titanate (PZT) to matrices, arranged from high to low singular values, and reconstruct the new time series (principal components) by diagonal averaging on determined matrices to characterize the essential dynamic characteristics of signals. MSE values of the principal components are used as damage index and adopted as input of genetic algorithm-based SVM to train a classifier to fulfill accurate monitoring of bolt joints. The theoretical derivation, application researches and comparison analysis can validate the effectiveness and superiority of the proposed approach in the field of bolt looseness monitoring.

Multiclass cyber-attack classification approach based on the Krill Herd Optimized Deep Neural Network (KH-DNN) model for WSN

The Intrusion Detection System (IDS) is a useful infiltration detection tool. With IDS, it may mechanically categorize intrusions, attacks, or breaches of security stratagems across host-level within the network and within the organization. The Krill Herd Optimized Deep Neural Network (KH-DNN) is proposed in this document as a multiclass cyberattack methodology. This model is suggested by uniting the network-based intrusion detection system (NIDS) and the host-based intrusion detection system (HIDS) to advance flexible and actual IDS to categorize and detect unexpected and unpredictable cyber attacks. In this method, two types of IDS are classified, that is, NIDS and HIDS. Classification of the system call is exploited to discriminate behavior into normal and attack categories. In this manuscript accuracy, the parameter of the deep neural network classifier is optimized with the Krill Herd optimization algorithm. Finally, the DNN model is performed in the NIDS dataset and the HIDS dataset. For the HIDS dataset, the KDD Cup 99 dataset was used, which employed two datasets: ADFA-LD and ADFA-WD, and several types of attacks were detected and classified, including Adduser, Hydra-FTP, Java-Meterpreter, Hydra-SSH, and Web-Shell. The suggested IDS-KH-DNN algorithm attains higher accuracy 42.56% and 23.4%, high precision 84.74% and 52.43%, high F-score 53.5% 64.455, high sensitivity 33.4% and 45.23%, and high specificity 31.45% and 44.23% for the NIDS dataset. The proposed system is compared to two current processes: intrusion detection using Gray Wolf optimization-based support vector machine (IDS-GWO-SVM) and intrusion detection using artificial bee colony optimization algorithm based support vector machine (IDS-ABCO-SVM). Finally, the simulation results show that the suggested approach is capable of quickly and accurately locating optimal solutions.

A Genetic Algorithm-Based Support Vector Machine Approach for Intelligent Usability Assessment of m-Learning Applications

In the field of human computer interaction (HCI), the usability assessment of m-learning (mobile-learning) applications is a real challenge. Such assessment typically involves extraction of best features of an application like efficiency, effectiveness, learnability, cognition, memorability, etc., and further ranking of those features for overall assessment of the quality of the mobile application. In the previous literature, it is found that there is neither any theory nor any tool available to measure or assess a user’s perception and assessment of usability features of a m-learning application for the sake of ranking of the graphical user interface of a mobile application in terms of a user’s acceptance and satisfaction. In this paper, a novel approach is presented by performing a mobile application’s quantitative and qualitative analysis. Based on the user’s requirements and perception, a criterion is defined based on a set of important features. Afterwards, for the qualitative analysis, genetic algorithm (GA) is used to score prescribed features for usability assessment of a mobile application. The used approach assigns a score to each usability feature according to the user’s requirement and weight of each feature. GA performs the rank assessment process initially by performing feature selection and scoring the best features of the application. A comparison of assessment analysis of GA and various machine learning models, i.e., K-nearest neigbors, Naïve Bayes, and Random Forests is performed. It was found that GA-based support vector machine (SVM) provides more accuracy in the extraction of best features of a mobile application and further ranking of those features.

Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of the developed models was evaluated based on statistical indices and graphical representations. The results of the gamma test based on the least value of gamma and standard error indices show that days of anthesis (DOA), days of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined to be an efficient information vector combination for the drought-tolerance index (DTI) as well as the stress-tolerance index (STI). The MLP, SVM, MLP-GA, and SVM-GA algorithms’ results were compared based on statistical indices and visual interpretations that have satisfactorily predict the drought-tolerance index and stress-tolerance index in maize crops. The genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found to better predict the drought-tolerance index and stress-tolerance index in maize crops. Similarly, the SVM-GA model was found to have the highest potential to forecast the DTI and STI in maize crops, compared to the MLP, SVM, and MLP-GA models.

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing.

As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of multi-channel signals. However, MMPE still has some intrinsic shortcomings that exist in the coarse-grained procedure, and it lacks the precise estimation of entropy value. To address these issues, in this paper a novel non-linear dynamic method named composite multivariate multi-scale permutation entropy (CMMPE) is proposed, for optimizing insufficient coarse-grained process in MMPE, and thus to avoid the loss of information. The simulated signals are used to verify the validity of CMMPE by comparing it with the often-used MMPE method. An intelligent fault diagnosis method is then put forward on the basis of CMMPE, Laplacian score (LS), and bat optimization algorithm-based support vector machine (BA-SVM). Finally, the proposed fault diagnosis method is utilized to analyze the test data of rolling bearings and is then compared with the MMPE, multivariate multi-scale multiscale entropy (MMFE), and multi-scale permutation entropy (MPE) based fault diagnosis methods. The results indicate that the proposed fault diagnosis method of rolling bearing can achieve effective identification of fault categories and is superior to comparative methods.

Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain

In this paper, a blockchain-based secure routing model is proposed for the Internet of Sensor Things (IoST). The blockchain is used to register the nodes and store the data packets’ transactions. Moreover, the Proof of Authority (PoA) consensus mechanism is used in the model to avoid the extra overhead incurred due to the use of Proof of Work (PoW) consensus mechanism. Furthermore, during routing of data packets, malicious nodes can exist in the IoST network, which eavesdrop the communication. Therefore, the Genetic Algorithm-based Support Vector Machine (GA-SVM) and Genetic Algorithm-based Decision Tree (GA-DT) models are proposed for malicious node detection. After the malicious node detection, the Dijkstra algorithm is used to find the optimal routing path in the network. The simulation results show the effectiveness of the proposed model. PoA is compared with PoW in terms of the transaction cost in which PoA has consumed 30% less cost than PoW. Furthermore, without Man In The Middle (MITM) attack, GA-SVM consumes 10% less energy than with MITM attack. Moreover, without any attack, GA-SVM consumes 30% less than grayhole attack and 60% less energy than mistreatment. The results of Decision Tree (DT), Support Vector Machine (SVM), GA-DT, and GA-SVM are compared in terms of accuracy and precision. The accuracy of DT, SVM, GA-DT, and GA-SVM is 88%, 93%, 96%, and 98%, respectively. The precision of DT, SVM, GA-DT, and GA-SVM is 100%, 92%, 94%, and 96%, respectively. In addition, the Dijkstra algorithm is compared with Bellman Ford algorithm. The shortest distances calculated by Dijkstra and Bellman are 8 and 11 hops long, respectively. Also, security analysis is performed to check the smart contract’s effectiveness against attacks. Moreover, we induced three attacks: grayhole attack, mistreatment attack, and MITM attack to check the resilience of our proposed system model.

A genetic algorithm-based support vector machine model for detection of hearing thresholds

ABSTRACT Auditory evoked potentials (AEPs), which are detected on the EEG auditory cortex area, are very small signals in response to a sound stimulus (or electric) from the inner ear to the cerebral cortex. These signals are recorded from electrodes attached to the scalp and are used for measuring the bioelectric function of the auditory pathway. In order to characterise their dynamic behaviour and due to the complex behaviour of nonlinear dynamic properties of EEG signals, several nonlinear analyses have been performed. In this work, Detrented Fluctuation Analysis (DFA) is applied to estimate the Fractal Dimension (FD) from the recorded AEP signals of the normal and the impaired hearing subjects. This aims at detecting their hearing threshold level. With the aim of classifying both groups, the normal and hearing impaired subjects, a hybrid approach based on the Support Vector machines (SVM) and genetic algorithms (GA) is taken: GA is applied to simultaneously optimise both SVM kernel parameters and feature subset selection. Our results indicate that the hybrid GA-SVM is promising; it is able to determine well the SVM kernel parameters along with feature subset selection, which will result in a high classification accuracy.

Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.