Bayes Algorithm Research Articles

Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer

ObjectivesThis study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC). MethodsA total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics model was evaluated using the ROC curve. A nomogram was constructed by incorporating the optimal radiomics signatures with significant clinical-radiological features and immunohistochemistry (IHC) markers obtained from preoperative curettage specimens. The diagnostic performance and clinical value of the nomogram were evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). ResultsAmong the radiomics models, the NB model, developed from 12 radiomics features derived from ADC and DCE4 sequences, exhibited strong performance in both training and validation sets, with the AUC values of 0.927 and 0.869, respectively. The nomogram, incorporating the radiomics model with significant clinical-radiological features and IHC markers, demonstrated superior performance in both the training (AUC = 0.951) and the validation sets (AUC = 0.915). Additionally, it exhibited excellent calibration and clinical utility. ConclusionsThe radiomics nomogram has great potential to differentiate type II from type I EC, which may be an effective tool to guide clinical decision-making for EC patients.

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

Early prediction of COVID-19 infection using data mining and multi machine learning algorithms

The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.

Gender Estimation from Morphometric Measurements of Mandibular Lingula by Using Machine Learning Algorithms and Artificial Neural Networks

Background: Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull. Aim: Our aim in this study is gender estimation with morphometric measurements taken from mandibular lingula, an important structure on the mandible, by using machine learning algorithms and artificial neural networks. Methods: Cone beam computed tomography images of the mandibular lingula were obtained by retrospective scanning from the Picture Archiving Communication Systems of the Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, İnönü University. Images scanned in Digital Imaging and Communications in Medicine (DICOM) format were transferred to RadiAnt DICOM Viewer (Version: 2020.2). The images were converted to 3-D format by using the 3D Volume Rendering console of the program. Eight anthropometric parameters were measured bilaterally from these 3-D images based on the mandibular lingula. Results: The results of the machine learning algorithms analyzed showed that the highest accuracy was 0.88 with Random Forest and Gaussian Naive Bayes algorithm. Accuracy rates of other parameters ranged between 0.78 and 0.88. Conclusions: As a result of the study, it is thought that mandibular lingula-centered morphometric measurements can be used for gender determination as well as bones such as the pelvis and skull as they were found to be highly accurate. This study also provides information on the anatomical position of the lingula according to gender in Turkish society. The results can be important for oral-dental surgeons, anthropologists, and forensic experts.

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

AbstractAnalyzing correlated high‐dimensional data is a challenging problem in genomics, proteomics, and other related areas. For example, it is important to identify significant genetic pathway effects associated with biomarkers in which a gene pathway is a set of genes that functionally works together to regulate a certain biological process. A pathway‐based analysis can detect a subtle change in expression level that cannot be found using a gene‐based analysis. Here, we refer to pathway as a set and gene as an element in a set. However, it is challenging to select automatically which pathways are highly associated to the outcome when there are multiple pathways. In this paper, we propose a semiparametric multikernel regression model to study the effects of fixed covariates (e.g., clinical variables) and sets of elements (e.g., pathways of genes) to address a problem of detecting signal sets associated to biomarkers. We model the unknown high‐dimension functions of multi‐sets via multiple Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, our variable set selection can be considered a Gaussian process set selection. We develop our Gaussian process set selection under the Bayesian variance component‐selection framework. We incorporate prior knowledge for structural sets by imposing an Ising prior on the model. Our approach can be easily applied in high‐dimensional spaces where the sample size is smaller than the number of variables. An efficient variational Bayes algorithm is developed. We demonstrate the advantages of our approach through simulation studies and through a type II diabetes genetic‐pathway analysis.

Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards

The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.

Deteksi Pola Kejadian Bencana Menggunakan Algoritma Naïve Bayes di Kabupaten Boyolali

Boyolali Regency, which is geographically located between Mount Merbabu and the still active Mount Merapi, does have high potential as a disaster-prone area. According to data from the Boyolali Regional Disaster Management Agency, during the period from 2022 to 2023, 936 disaster events were recorded in the region. These disasters have a devastating impact on ecosystems and the environment, causing significant material losses, causing psychological disorders, and even threatening human safety. In this context, detecting patterns of disaster events is an important step in disaster mitigation and management efforts. This research was conducted with the aim of understanding patterns of disaster events based on data collected by BPBD Boyolali. The method used is the Naive Bayes algorithm, which has proven to be a powerful tool in data analysis. The results of this research show a high level of accuracy of 92.52%, precision of 92.68%, and recall of 91.64% using the Naive Bayes algorithm. This indicates that the algorithm is effective in recognizing and classifying patterns of disaster events based on existing data. Thus, the results of this research can provide a valuable contribution to the development of an early disaster detection system, as well as assist the authorities in designing more effective mitigation strategies to protect the community and environment in Boyolali Regency.

Fake News Detection

The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework's effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.

Enhancing outlier detection in air quality index data using a stacked machine learning model

AbstractThe air quality index (AQI) is a commonly employed metric for evaluating air quality across diverse locations and temporal spans. Similar to other environmental datasets, AQI data can exhibit outliers data points markedly divergent from the norm, signifying instances of exceptionally favorable or adverse air quality. This becomes crucial in identifying and comprehending severe pollution episodes with far‐reaching environmental and public health implications. This study utilizes air quality data from January 1, 2014, to January 31, 2021, collected at daily intervals in Shanghai City, China, as the experimental dataset. The dataset includes daily AQI measurements, along with six pollutant concentrations: particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Each pollutant's concentration is measured in micrograms per cubic meter (g/m). The dataset is then preprocessed by cleaning and normalizing it before using K‐means clustering to discover different patterns. A stacked ensemble machine learning model that incorporates K‐means clustering, random forest (RF) and gradient boosting classifier (GBC) is developed and compared to decision tree, support vector machine, K‐nearest neighbor and Naive Bayes algorithms to evaluate its performance in identifying outliers using accuracy, precision, recall, and F1‐score. The stacked model outperformed all other established models based on the accuracy, precision, recall, and F1‐score of 0.99, 0.99, 0.97, and 0.99, respectively.

Classification Of Malaria Types Using Naïve Bayes Classification

This study was conducted to determine the level of accuracy of the naïve bayes classification method in the process of determining the group type of malaria. This method is used to predict the category of malaria based on the symptoms displayed. In this study, the dataset used was divided into 60% for training and 40% for testing. The results showed that the naïve bayes algorithm had an accuracy rate of 99.8% in predicting malaria categories. Evaluation of model performance using confusion matrix and ROC curve also showed good results, with classification accuracy of 0.998, error 0.002, and AUC 0.999. The results of the classification report show that the Quartana, Tertiana, and Tropica categories are more dominant than the Ovale category, based on precision, recall, and f1-score. These results show that the naïve bayes classification method is effective in classifying types of malaria and can be used as an aid in the diagnosis of malaria.

Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning

AbstractObjectivesDocumentation and billing are important and time‐consuming parts of an otolaryngologist's work. Given advancements in machine learning (ML), we evaluated the ability of ML algorithms to use operative notes to classify rhinology procedures by Current Procedural Terminology (CPT®) code. We aimed to assess the potential for ML to replicate rhinologists' completion of their administrative tasks.Study DesignRetrospective cohort study.SettingUrban tertiary hospital.MethodsA total of 594 operative notes from rhinological procedures across six CPT codes performed from 3/2017 to 4/2022 were collected from 22 otolaryngologists. Text was preprocessed and then vectorized using CountVectorizer (CV), term frequency‐inverse document frequency, and Word2Vec. The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. Model‐classified CPT codes were compared to codes assigned by operating surgeons. Model performance was evaluated by area under the receiver operating characteristic curve (ROC‐AUC), precision, recall, and F1‐score.ResultsPerformance varied across vectorizers and ML algorithms. Across all performance metrics, CV and NB was most overall the best combination of vectorizer and ML algorithm across CPT codes and produced the single best AUC, 0.984.ConclusionsIn otolaryngology applications, the performance of basic ML algorithms varies depending on the context in which they are used. All algorithms demonstrated their ability to classify CPT codes well as well as the potential for using ML to replicate rhinologists' completion of their administrative tasks.

Implementation Of The Naïve Bayes Method With Approach Holistic In Predicting Student Achievement

Within the realm of education, students frequently encounter many obstacles, including defiance, absence of drive, and other intricate factors that can impact the caliber of their learning. This issue can result in many students abstaining from attending class, withdrawing from school, or prematurely leaving before completing their education. This highlights the need for proactive and preventive measures taken by educational institutions to assist children in overcoming learning barriers from a young age. Professional educational and training institutions should promptly identify potential obstacles to students' learning and take proactive measures to assist them in overcoming these difficulties. These endeavors encompass vigilant supervision, effective communication among educators, parents, and students, as well as impactful mentoring and counseling initiatives. By adopting this methodology, educational establishments can establish an all-encompassing and nurturing atmosphere that prioritizes the academic achievement of students. In this study, we employ the Naive Bayes algorithm and the Holistic Approach, two powerful tools in the field of data analysis, to forecast student academic performance at Otista Bandung Vocational School. The Naive Bayes algorithm allows us to make predictions based on the probability of an event occurring given certain conditions, while the Holistic Approach ensures that we consider all relevant factors in our analysis. This research aims to offer early forecasts for students who are susceptible to low academic performance, enabling schools to proactively intervene and avert a fall in student success. By implementing this approach, schools may offer targeted support to individuals requiring more help, while also fostering an inclusive environment that promotes academic success for all students, regardless of their circumstances.

Analisis dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE

Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.

Method and System for Predicting Crop Yields and Recommending Fertilizers using Machine Learning Algorithms

In the recent years ensuring food security plays a major role in the agricultural sector and contributing towards the nations growth. This paper presents a communal system that makes use of elegant machine learning techniques and models to forecast accurate yield of the selected crop and advocates the felicitous fertiliser. By exploiting the agricultural datasets, our system employs the Extra Trees Regressor trees which helps for the prediction of the yield and to analyse the most recommendable fertilizer it makes use of the Gaussian Naïve Bayes algorithm. This dormant system provides the us with powerful insights. Our sight is to reshape the conventional agricultural practices with the help of these powerful insights to redefine the farming practices and to increase the productivity, therefore ensuring the legitimate agricultural practices.

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Objectives: The main objective in Sentiment Analysis, and financial data is to decode sentiments through the use of Natural Language Processing (NLP) and Machine Learning (ML). This provides insights into market trends that are essential for risk management and well-informed financial decisions. By removing emotion from financial writing, gives stakeholders practical advice for wise investments in dynamic markets. The main objective is to increase the accuracy of Multinomial Naïve Bayes (Multinomial NB). In this work, we used purely ML models to get the accuracy more than the previous works, which got 74%, and in this work, we can see the accuracy increased to approximately 82%. Methods: This specific Research Work employs Machine Learning approaches to extract sentiment polarity (positive, negative, and neutral) from financial text data. The used ML models are- Multinomial Naïve Bayes (Multinomial NB), Logistic Regression (LR), KNeighbors, Decision Tree, and Random Forest algorithms are used for Sentiment Analysis. Findings: Studies conducted in the financial sector have shown that sentiment and informational substance in stock news have a big impact on market factors such as trading volume, volatility, stock prices, and corporate earnings. This study involves approximately 6000 newspaper articles to extract the polarity from financial text data. The incorporation of Multinomial NB of Natural Language Processing techniques results in a marginal improvement in the sentiment classification model performance among all the models used, where the previous works got the lowest accuracy of 74%. Novelty: This research introduces advanced ML models for extracting financial sentiment from text, including Random Forest, LR, KNeighbors, Decision Tree, and Multinomial NB previously unexplored in similar studies. Notably, the Multinomial NB model accuracy rose significantly from 65% to 82%. The study also presents sentiment segregation (positive, negative, neutral) visualized in a donut chart. Keywords: Sentiment Analysis, Machine Learning, Natural Language Processing, Financial data, Stock market prediction

Research on E-learning interactive English vocabulary recommendation education system based on naive Bayes algorithm

E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive Bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. Decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naive Bayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.

Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development

Introduction: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention. Methods: This study included 2,288 participants with normal cognitive function from the Ma’anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level. Results: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773–0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults. Conclusion: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.

Analisis Perbandingan Klasifikasi Penyakit Jantung Menggunakan Algoritma Nai ̈ve Bayes dan Algoritma Logistic Regression

Heart disease is a condition where parts of the heart are damaged. Thus, early detection is needed. One of them is by doing data mining classification using the Naïve Bayes and Logistic Regression algorithms. This research will compare Naïve Bayes and Logistic Regression algorithms through the training percentage split and k-fold cross validation methods to get the best classification results in detecting heart disease by calculating the average value of precision, recall, and accuracy. The Naïve Bayes algorithm with the training percentage split method produces average values for precision, recall, and accuracy of 83%, 82.5% and 81%, while the Naïve Bayes algorithm with k-fold cross validation provides average values for precision, recall, and accuracy of 83.5%, 85.5% and 83%. Logistic Regression algorithm with percentage split training method produces average values for precision, recall, and accuracy of 73.5%, 73.5% and 73%, while Logistic Regression algorithm using k-fold cross validation produces average values for precision, recall, and accuracy of 84%, 83.5% and 84%. This shows that the Naïve Bayes algorithm using percentage split is better than Logistic Regression, but when using the k-fold cross validation method, the Logistic Regression algorithm has a significant increase compared to Naïve Bayes. So that to classify heart disease is better with the Logistic Regression Algorithm with the k-fold cross validation method.

ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach

While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.