Support Vector Machine Learning Algorithm Research Articles

A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

In order to achieve the highly efficient and accurate identification of fracture modes including tension or shear fractures during rock failure, an intelligent identification method based on Wigner-Ville distribution (WVD) spectrogram features of acoustic emission (AE) signals was proposed. This method was mainly constructed by the following steps: Firstly, AE hits corre-sponding to tension and shear fractures were obtained through conducting the Brazilian disc test (tension fracture) and direct shear test (shear fracture) of limestone. Secondly, the WVD spectro-grams of these tensile-type and shear-type AE hits were respectively extracted and then trans-formed into the image features of relatively low-dimension as the sample set based on the gray-level cooccurrence matrix (GLCM) and histogram of oriented gradient (HOG). Finally, on the basis of the processed and classified sample set of the WVD spectrogram features, an identifica-tion model of rock fracture modes was established by a support vector machine (SVM) learning algorithm. To verify this method, the fracture modes of limestone subjected to biaxial compres-sion were identified by the method. The results showed that the method not only can greatly re-veal the fracture modes change from tension-dominated to shear-dominated fractures, but also has advantages over the RA-AF value method, such as applicability, accuracy and practicality.

Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm.

Phacomatosis pigmentokeratotica (PPK), an epidermal nevus syndrome, is characterized by the coexistence of nevus spilus and nevus sebaceus. Within the nevus spilus, an extensive range of atypical nevi of different morphologies may manifest. Pigmented lesions may fulfill the ABCDE criteria for melanoma, which may prompt a physician to perform a full-thickness biopsy. Excisions result in pain, mental distress, and physical disfigurement. For patients with a significant number of nevi with morphologic atypia, it may not be physically feasible to biopsy a large number of lesions. Optical coherence tomography (OCT) is a non-invasive imaging modality that may be used to visualize non-melanoma and melanoma skin cancers. In this study, we used OCT to image pigmented lesions with morphologic atypia in a patient with PPK and assessed their quantitative optical properties compared to OCT cases of melanoma. We implement a support vector machine learning algorithm with Gabor wavelet transformation algorithm during post-image processing to extract optical properties and calculate attenuation coefficients. The algorithm was trained and tested to extract and classify textural data. We conclude that implementing this post-imaging machine learning algorithm to OCT images of pigmented lesions in PPK has been able to successfully confirm benign optical properties. Additionally, we identified remarkable differences in attenuation coefficient values and tissue optical characteristics, further defining separating benign features of pigmented lesions in PPK from malignant features.

EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.

The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements; left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz; training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index.

Predictive Analysis in Banking using Machine Learning

This paper discusses the utilization of machine learning for predicting loan approval and credit card fraud detection. Specifically, the paper proposes the use of the Random Forest Algorithm and Support Vector Machine Learning Algorithm for achieving better accuracy. The banking sector's main objective is to ensure their assets are in safe hands, and to achieve this, a verification process is carried out. However, the process takes a long time, and there is no guarantee of selecting deserving applicants. To address this problem, a system has been developed that predicts the suitability of an applicant for loan approval based on a model trained using machine learning algorithms. The system has achieved 92% accuracy using the Random Forest Algorithm. The system has a user interface web application where users can input necessary details for the model to predict. The system's drawback is that it considers multiple attributes, whereas, in real life, a loan application may be approved based on a single strong attribute, which the system cannot detect. With the increasing number of online transactions, credit card usage has become more prevalent. Losing physical credit cards or credit card information can result in significant financial loss. Therefore, there is a need to detect fraudulent transactions and secure them. To address this issue, the paper proposes the use of the Support Vector Machine Algorithm, which focuses on analyzing and preprocessing data sets and deploying fraud detection using Credit Card Transaction data.

Experimental comparison of R290 and R600a and prediction of performance with machine learning algorithms

The use of alternative refrigerants is among the popular topics of the refrigeration industry. In the first part of this study, thermodynamic performances of R290 and R600a gases were compared in a vapor compression refrigeration experiment setup. Although R600a caused an average of 33.44% less compressor power consumption compared to R290 refrigerant, R290 provided an average of 23.77% increase in COP (coefficient of performance), 82.55% in cooling capacity, and 20.99% increase in second law efficiency compared to R600a. In the second part of the study, the performance parameters of the refrigeration system were predicted with MLP (multi-layer perceptron), SVM (support vector machine), and DT (decision tree) machine learning algorithms. It was detected that the SVM method predicted all parameters with the least error. MAE (mean absolute error) values detected in the COP prediction with test set were 0.0317, 0.0324, and 0.0989 for SVM, MLP, and DT, respectively. Results revealed that performance of the refrigeration system increased when utilizing R290, and SVM was superior in prediction of performance indicators compared to other machine learning methods.

SENTIMENT ANALYSIS OF STUDENTS’ FEEDBACK ON INSTITUTIONAL FACILITIES USING TEXT-BASED CLASSIFICATION AND NATURAL LANGUAGE PROCESSING (NLP)

In this study, sentiment analysis, commonly referred to as opinion mining or emotional artificial intelligence (AI), is used to obtain student comments about institutional facilities. To ascertain whether an online text has a good, negative, or neutral emotional tone, it must first be analyzed. Natural Language Processing (NLP) includes the subfield of sentiment analysis, and NLP can be used to categorize and extract information with the aid of machine learning methods. Finding out if students are content with the amenities or services provided is important since, in an educational setting, they are consumers. This study of the resources and services that the libraries offer evaluated college students' perceptions of the books, audio CDs, and video CDs, the services provided by the library staff, and the personal computers that are made available to them as part of their facilities. In the current study, surveys were carried out to gather information and assess how well the needs of the students are addressed. On Kaggle.com, secondary data from a North Indian institution was used in the experiments. The research approach was sentiment analysis using a machine learning framework. The F1-score, a harmonic mean of precision and recall based on the attitudes evaluated by the algorithms, was calculated using a text-based classification method using Naive Bayes Multinomial and Support Vector machine learning algorithms. The findings offer views on how successfully library facilities are managed in the form of student sentiments.

Wavelet multiscale principal components and traveling waves enabled machine learning technique for protection of MT-HVDC systems

The interconnection of renewable energy sources to conventional power grids is widely recognized and promoted by the deployment of high voltage direct current (HVDC) technology. One of the biggest challenges associated with the HVDC systems is the rapid rate of rising DC fault currents, resulting in the immediate collapse of the converter stations. Conventional schemes of AC protection and low-speed DC protection are not worthy and mature because of the abrupt rising characteristics of DC currents. Therefore, protection techniques are being developed for reliable bulk power transfer and interconnection of unsynchronized grids via DC links. Timely identification, classification, and determination of location are the most fundamental characteristics of any protection scheme. This research paper proposes a machine learning-based protection method that establishes a rapid and reliable solution addressing the drawbacks of existing protection schemes. A multi-terminal high voltage direct current (MT-HVDC) test system is designed in Matlab/Simulink. Pole-to-ground and pole-to-pole faults are applied at different fault locations to assess the validity of the proposed algorithm. For this, DC voltages and currents are measured and analyzed. Power spectral analysis of traveling waves and Wavelet multiscale principal components analysis are employed for the extraction of featured data to classify and locate the DC faults. This reduced dimensional featured data is utilized for training and testing the support vector machine learning algorithm to analyze the accuracy of the proposed protection technique. Simulation results confirm the accuracy of rapid identification, classification, and location of DC faults.

Using acoustic feedback and analyses for removal of bone cement during revision knee replacement surgeries.

The purpose of this study was to analyze the scraping sounds generated during revision knee replacement surgeries to discriminate between the inner cortical bone and the cement, with the goal of minimizing bone removal and increasing the structural integrity of the revision. We prepared seven porcine femurs by partially filling them with bone cement, and recorded scraping sounds produced by a surgical scraping tool. We used a hierarchical machine learning approach to first detect a contact and then classify it as either bone or cement. This approach was based on a Support Vector Machine learning algorithm that was fed with temporal and spectral features of the sounds. A Leave-One-Bone-Out validation method was used to assess the performance of the proposed method. The average recall for the noncontact, bone, and cement classes was 98%, 75%, and 72%, respectively. The corresponding precision for the respective classes was 99%, 67%, and 61%. The scraping sound that is generated during revision replacement surgeries carries significant information about the material that is being scraped. Such information can be extracted using a supervised machine learning algorithm. The scraping sound produced during revision replacement procedures can potentially be used to enhance cement removal during knee revision surgery. Future work will assess whether such monitoring can increase the structural integrity of the revision.

Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms

Lake Tana is Ethiopia’s largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity and habitat. Using appropriate remote sensing detection methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and environmental protection. As the demand for the reliable estimation of E. crassipes mapping from satellite data grows, comparing the performance of different machine learning algorithms could help in identifying the most effective method for E. crassipes detection in the lake. Therefore, this study aimed to examine the ability of random forest (RF), support vector machine (SVM), and classification and regression tree (CART) machine learning algorithms to detect E. crassipes and estimating seasonal spatial coverage of the weed on the Google Earth Engine (GEE) platform using Landsat 8 and Sentinel 2 images. Cloud-masked monthly median composite Landsat 8 and Sentinel 2 data from October 2021 and 2022, January 2022 and 2023, March 2022, and June 2022 were used to represent autumn, winter, spring, and summer, respectively. Four spectral indices were derived and used in combination with spectral bands to improve the E. crassipes detection accuracy. All methods achieved greater than 95% and 90% overall accuracy when using Sentinel 2 and Landsat 8 images, respectively. Using both data sets, all methods achieved a greater than 93% F1 score for E. crassipes detection. Though the difference in performance between the methods was small, the RF was the most accurate, while the SVM and CART methods had the same accuracy. The maximum E. crassipes coverage area was observed in autumn (22.4 km2), while the minimum (2.2 km2) was observed in summer. Based on Sentinel 2 data, the E. crassipes area coverage decreased significantly by 62.5% from winter to spring and increased significantly by 81.7% from summer to autumn. The findings suggested that the RF classifier was the most accurate E. crassipes detection algorithm, and autumn was an appropriate season for E. crassipes detection in Lake Tana.

Does herding effect help forecast market volatility?—Evidence from the Chinese stock market

AbstractThis paper aims to study the predictive power of market herding effect for market volatility. We extend the widely used four types of linear HAR models by incorporating the market herding index and further introduce a nonlinear forecasting mechanism by using two machine learning algorithms—support vector regression (SVR) and random forest (RF). All the methods are applied to the Chinese stock market. We evaluate their in‐sample and out‐of‐sample performances for various prediction step lengths. The results show that according to the RMSE and MAE, for the daily volatility prediction, the extra herding factor makes no significant contribution, and the linear model has no significant difference with the algorithms as well, whereas with the increase of step length, the herding factor shows a significant power in the weekly, monthly, and quarterly prediction, and meanwhile, the RF algorithm outperforms the other methods. However, for the QLIKE, the herding factor always significantly improves the predictions for all kinds of step lengths and methods, and the SVR algorithm with the herding factor is dominant. Overall, the herding factor helps forecast the volatility, and the extent to which the precision is improved takes on an inverted “U” shape with the increase in step length. The two algorithms are superior to the linear model, especially for the longer‐term prediction, and the herding factor can help enlarge this advantage, which implies a nonlinear relationship between the herding and volatility. Besides, the extra liquidity factor plays no significant role in the volatility prediction, but the continuous variation (CV), jump variation (JV), and leverage effect are helpful in most cases.

Data Mining Techniques in Telecommunication Company

Due to emerging of amalgam amount of data from variety sources, the data mining has become a hot trend in field of Computer Science. Data mining extracts useful pattern and information from huge amount of existing data with the help of machine learning algorithms that can be helpful in solving many sophisticated problems.
 Telecommunication companies also generates big amount of data from providing services to their customers, besides that telecommunication companies suffers from many problems like fraud, Customer churn and …etc.
 The generated amount of data from these companies can help them to address the solution for their problems such as Customer Churn. Customer churn indicates to the event when a customer stops using the service of a company and starts to use the service of another company.
 Churning of a Customer plays a vital role in having a sustainable business development for a telecommunication company since attracting new customers do not profit a company without retaining the old ones.
 Data mining can address the problem by predicting the occurrence of customer churn in Telecom Company, which helps the company to be proactive in this event and can have the chance to retain them before the churn occurs.
 In this study, I have chosen two open Telecom Churn data sets and have applied Support Vector Machine, Logistic Regression and Decision Tree Machine Learning Algorithms on each data sets independently, which conclude my work to six experiments.
 I have used k-fold cross validation as validation technique during my experiments and confusion matrix for calculating the accuracy of each algorithm, the result of experiments will provide the accuracy of each algorithm in churn prediction for each data set.
 At the end we will have a general comparison table from all six experiments which will show the algorithms performance summary and will indicate which algorithm will outperform the others.

Single-molecule identification of folded proteins from nanopore ionic current signatures.

Single-molecule protein sequencing would provide unprecedented insights into cellular processes and the diseases that arise from protein misfunction. Recently, nanopores have emerged as a platform for fast label-free analyte detection guided by the ionic current blockades generated from single molecules translocating under an applied electric field. The major bottleneck to nanopore protein sequencing is the need to distinguish twenty proteinogenic amino acids accurately at every single residue. Here, we demonstrate a proof-of-principle proteome characterization system that relies on ionic current signatures produced by complete translocation of natively folded proteins through an array of nanopores. Using the steric exclusion model, we computed the ionic current spectra for all structurally known proteins, initially excluding all membrane proteins, proteins containing partially-disordered domains, and multimeric protein assemblies - about 4000 proteins in total. For each protein, we computationally simulated a fluid driven tumbling through a nanopore by calculating current for 5000 randomly chosen orientations that changes the effective cross-section and thus modulates current. Ionic current signatures of proteins with varying molecular weight clearly differed by the magnitude of blockades confirming the steric exclusion origin of the current. Interestingly, proteins with similar molecular weight also showed a varied range of mean blockade currents due to changing current signatures originating from the protein shape. Using a support vector machine learning algorithm trained on our data set, we were able to theoretically distinguish the weight and shape of the 4000 chosen proteins to near perfect accuracy, in the absence of instrumentation noise. We then theoretically introduced a thermal noise source into our simulated ionic current recordings to demonstrate the minimum requirements of an experimental system that would permit accurate protein identification from a single molecule translocation.

Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine)

The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectra.

Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics.

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T2-weighted imaging, T1-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T1-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.

Violence Detection with Machine Learning: A Sociodemographic Approach

This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India

Mapping the groundwater potential zones with high accuracy is always a difficult task. The combination of Geographic Information System (GIS) and Remote Sensing (RS) with machine learning techniques provide a reliable method to map the groundwater prospective areas. This research used support vector machine (SVM) learning and random forest (RF) regression algorithms to predict the groundwater potential areas in the Bundelkhand craton region. All the parameters that affect the groundwater occurrence in this region, such as altitude, aspect, distance to drainage, distance to road, distance to faults, geomorphology, distance to river, lithology, normalized difference vegetation index (NDVI), rainfall, slope, soil, drainage density, land use land cover (LULC), topographic wetness index (TWI), lineament density and curvatures have been prepared by the remote sensing data as well as data taken from different departments and organizations. The training and testing dataset is generated from the groundwater potential zones map prepared through the frequency ratio (FR) technique. A total of 23,917-pixel locations have been selected in the research region. These locations contain the location of groundwater points and non-groundwater points equally. These points were randomly portioned into 70:30 for training and testing the model, respectively. The 2417 unknown points were taken from the study area and given to the trained model to predict the groundwater prospective areas. The maps of groundwater potential zones obtained using machine learning models were categorized into five classes: very low, low, moderate, high, and very high. The outcome of the employed algorithm is validated through the well discharge data and area under the receiver operating characteristics curve (AUC-ROC) method. The developed model's outcome gives valuable information regarding the effective management of groundwater in a particular region to government agencies and private sectors.

Development of a Smart Sensor Array for Adulteration Detection in Black Pepper Seeds using Machine Learning

Black pepper is an expensive commodity with a high risk of adulteration. Ground papaya seed is the main adulterant in pepper because it cannot be discriminated visually. There are few destructive methods. Since pepper is costlier, non-destructive method of adulteration is must but it is challenging one. The existing non-destructive method uses costlier equipment, bulky, involve laboratory-based testing, time consuming in the process. To overcome the above issues, this article presents the development of Non-destructive E- nose gas sensor for pepper adulteration detection. This system determines the VOC in a controlled environment. The proposed system utilizes MQ2 and MQ3 gas sensor arrays to identify Volatile Organic Compounds present in pepper seeds to discriminate adulterant and non-adulterant sample. The sensor data are utilized to perform the qualitative analysis to determine the adulteration using a support vector machine learning algorithm. The proposed sensor system with Support Vector Machine learning algorithm outperforms in comparison with existing methods with 100% classification accuracy. Conclusion: The developed gas sensor system is connected to the internet via the IoT application model to show results on the web pages and enables access by the authenticated user from anywhere. Client server model with MQTT protocol is used for developing IoT application.

Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest.

Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms. In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research. All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support. In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

An Internet of Things-based Efficient Solution for Smart Farming

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved.

Improving Lyme disease testing with data driven test design in pediatrics

Diagnostic advances have not kept pace with the expansion of Lyme disease caused by Borrelia burgdorferi and transmitted by ticks. Lyme disease clinical manifestations can overlap with many other diagnoses making Lyme disease a critical part of many differential diagnoses in endemic areas. Current diagnostic blood tests rely on a 2-tiered algorithm for which the second step is either a time-consuming western blot or a whole cell lysate immunoassay. Neither of these second step tests allow for rapid results of this critical rule out test. We hypothesized that using western blot confirmation information, we could create computational models to propose recombinant second-tier tests that would allow for more rapid, automated, and specific testing algorithms. We propose here a framework for assessing retrospective data to determine putative recombinant assay components. A retrospective pediatric cohort of 2755 samples submitted for Lyme disease screening was assessed using support vector machine learning algorithms to optimize tier 1 diagnostic thresholds for the Vidas IgG II assay and determine optimal tier 2 components for both a positive and negative confirmation test. In cases where the tier 1 screen was negative, but clinical suspicion was high, we found that 1 protein (L58) could be used to reduce false-negative results. For second-tier testing of screen positive cases, we found that 6 proteins could be used to reduce false-positive results (L18, L39M, L39, L41, L45, and L58) with a final machine learning classifier or 2 proteins using a final rules-based approach (L41, L18). This led to an overall accuracy of 92.36% for the proposed algorithm without a final machine learning classifier and 92.12% with integration of the machine learning classifier in the final algorithm when compared to the IgG western blot as the gold-standard. Use of this framework across multiple assays and institutions will allow for a data-driven approach to assay development to provide laboratories and patients with the improvements in turnaround time needed for this testing.