A hybrid intelligent system for medical data classification

Prediction of the diseases are possible using medical diagnosis system. This type of health care model can be developed using soft computing techniques. Hybrid approaches of data classification and optimization algorithm increases data classification accuracy. This research proposed applications of Moth Flame optimization (MFO) and Fuzzy Min Max Neural Network (FMMNN) for the development of medical data classification system. Here MFO algorithm considers bulk of features from the disease dataset and produces optimized set of features based on fitness function. MFO is able to avoid local minima problem and this is the main cause behind production of optimal set of features. Optimized features are then passed to FMMNN for classification of malignant and benign cases. As classification is concerned, model experiment achieved 97.74% accuracy for Liver Disorders and 86.95 % accuracy for Pima Indian Diabetes dataset. Improving the medical data classification accuracy is directly related to attain good human health.

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability---plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM---CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM---CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability---plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM---CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM---CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

Machine learning approach to predict susceptible growth regions of Moringa peregrina (Forssk)

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0–20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale.

A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification

This paper contributes to the ongoing debate over Likert scale experiments, in particular the issues of how to treat acceptability judgment data (as ordinal or interval) and what statistical model is appropriate to apply. We analyze empirical data on native speakers’ intuitions regarding marginal change-of state verbs in Russian (e.g. ukonkretit’ ‘concretize’, ovnešnit’ ‘externalize’) and compare the outcomes of five statistical models (parametric and non-parametric tests): (1) ANOVA; (2) Ordinal Logistic Regression Model; (3) Mixed-Effects Regression Model for Ordinal data; (4) Regression Tree and Random Forests Model; and (5) Classification Tree and Random Forests Model. We make four claims: (1) all five models are appropriate for this data to a greater or lesser degree; (2) overall, the outcomes of parametric and non-parametric tests applied to this data provide comparable results; (3) Classification Tree and Random Forests Model is the most appropriate, informative, and user-friendly regarding this data; and (4) the use of a culturally entrenched grading scale is an advantage.

An automatic microcalcification detection system based on a hybrid neural network classifier

BackgroundTranscutaneous transhepatic gallbladder drainage (PTGBD) has shown significant efficacy in the treatment of elderly patients with acute cholecystitis. The goal of this study is to develop a machine learning-based web calculator aimed at predicting the optimal timing for cholecystectomy (LC) after PTGBD in elderly patients with acute cholecystitis (AC) to achieve precise personalized medicine.MethodsA retrospective analysis of 979 elderly patients with acute cholecystitis admitted to Jinzhou Central Hospital and the First Affiliated Hospital of Jinzhou Medical University from 2013 to 2024 was performed, and a total of 680 patients were included in the model development. Patients were divided into delayed (347 cases, surgery > 6 weeks post-PTGBD) and non-delayed (333 cases) groups based on the interval between PTGBD and LC. Minimal Absolute Contraction and Selection Operator (LASSO) and logistic analysis were used to determine the predictors of postponement of LC in elderly patients with AC after PTGBD. Next, we used eight ML algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme gradient boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), to train and develop ML models using a 10x cross-validation method. The performance of the model was evaluated by a variety of indicators, including the area under the receiver operating characteristic curve (ROC), calibration curve, decision curve, PR curve, and confusion matrix. In addition, model interpretation is performed through Shapley Additive Interpretation (SHAP) analysis to clarify the importance of each feature of the model and its basis for decision-making. Finally, we chose to use the best model to develop a web-based calculator that could be used to predict the likelihood of delaying LC after PTGBD in elderly AC patients.ResultsIn multivariate logistic regression analysis, age, sex, gallbladder wall thickness, time between onset and PTGBD, white blood cell count (WBC), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR) were identified as independent predictors of delayed LC in elderly patients with AC after PTGBD. In the training set, the area under the receiver operating characteristic curve (AUC) values for these models ranged from 0.808 to 0.914, with the random forest (RF) model showing the highest AUC value. Through the evaluation of decision curve analysis (DCA), precision-recall (PR) curve and calibration curve, the RF model showed superior clinical decision support and prediction performance compared with the other seven models. Finally, we used the RF model to build an online network calculator, which aims to accurately assist doctors in making more informed and accurate clinical decisions and promote the wide application of the model in clinical practice (https://zw17786325639.shinyapps.io/Postpone/).ConclusionsThis study developed and validated an RF model network calculator based on clinical indicator information to assess the likelihood of delaying LC after PTGBD in elderly patients with acute cholecystitis. This tool is expected to assist physicians in making more appropriate clinical decisions for patients.

Introducing two Random Forest based methods for cloud detection in remote sensing images

Poyang Lake is not only a globally important stopover for migratory birds and a habitat for fish, but also one of the main sand mining areas in China. However, sand mining activities significantly affect the suspended sediment concentration in Poyang Lake, thereby impacting water quality, altering lake bed topography, and potentially disturbing the living environment of plants and animals. Therefore, understanding the suspended sediment concentration and the spatiotemporal patterns of sand mining activities in Poyang Lake holds vital significance for effective lake management and biodiversity conservation. Utilizing surface reflectance (SR) data derived from Landsat satellite imagery sourced via Google Earth Engine spanning from 1989 to 2018, coupled with SSC data recorded by daily observation at Hukou hydrological stations, we conducted a comparative analysis of five distinct methods: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Back Propagation Neural Network (BPNN). Moreover, the applicability of machine learning methods was analyzed and compared across four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04, Landsat 5, 7, and 8). Using the optimal SSC estimation model, we investigated the spatial and temporal trends of sand mining activities in Poyang Lake from 1989 to 2021, along with their potential impact on suspended sediment levels in the water body. The results showed that the prediction accuracy of machine learning is better than that of linear regression models, and the RF model has the best performance. The RF model generated R<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2} \gt 0.9$ </tex-math></inline-formula> for four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04 Landsat 5, 7, and 8) and showed little to no overfitting. The generated SSC map can clearly show the distribution of SSC in Poyang Lake, unveiling the impact of sand mining activities on suspended sediment, especially around sand dredgers where SSC can exceed 0.15 g/L. Sand mining activities in Poyang Lake emerged after 2000 and gradually shifted southward and expanded, reaching a peak in 2016. Fortunately, under government regulation, illegal sand mining has been effectively controlled and is currently concentrated near Songmenshan Island. Despite the fact that sand mining has been brought under control, there remains a necessity for heightened oversight to prevent any impact on national nature reserves. The Random Forest (RF) model demonstrates significant potential in utilizing Landsat satellite data to predict SSC in Poyang Lake, as well as to monitor sand mining activities in the area.

Eğitim, her birey ve bir bütün olarak toplum için ekonomik, sosyal ve kültürel gelişimin temelidir. Ülkemizde orta öğretim kurumlarına Milli Eğitim Bakanlığı tarafından yapılan lise giriş sınavı ile öğrenci kabul edilmektedir. Bu çalışmada, 2019 yılında Türkiye&#039;nin 81 ilinde lise giriş sınavına giren öğrencilerin başarı oranları makine öğrenimi regresyon ve beta regresyon modeli ile ele alınmıştır. Bu makale, boşanma oranı, gayri safi yurtiçi hasıla, okuma yazma bilmeyenlerin sayısı ve yüksek öğretim nüfusu sayısı gibi değişkenleri kullanarak öğrencilerin başarı oranlarını modellemeyi, tahmin etmeyi ve açıklamayı amaçlamaktadır. Başarı oranlarını tahmin etmek için destek vektörü regresyon, rastgele orman, karar ağacı ve beta regresyon modeli uygulanmıştır. En yüksek R2 değerine sahip iki modelin beta regresyon ve rastgele orman modelleri olduğu bulunmuştur. Beta regresyon ve rastgele orman modelinin tahmin hataları incelendiğinde, başarı oranlarını tahmin etmede rastgele orman modelinin beta regresyon modeline göre nispeten üstün olduğu görülmektedir. Beta regresyon modeli Çanakkale ilinin başarı oranlarının en iyi yordayıcısı iken, rastgele orman modeli Ankara&#039;nın başarı oranlarını iyi tahmin etmiştir. Ayrıca beta regresyon modelinde başarı oranları için anlamlı bulunan değişkenlerin rastgele orman modelinde de önemli olduğu görülmüştür. Öğrencilerin başarı oranlarını tahmin etmek için hem beta hem de rastgele orman modellerinin kullanılması önerilir.

Improving classification performance is an essential task in medical data classification. In the current medical data classification technique, if data pre-processing is not performed, the approach is more time consuming and has less classification accuracy. Here, the authors proposed two pre-processing techniques for enhancing the classification performance on medical data. The first pre-processing technique is noise filtering to improve the data quality. The second pre-processing bag of words technique is used for better feature selection. Subsequently, the hybrid fuzzy neural network approach is used for classification to handle data imprecision during classification. This arrangement of data pre-processing and the fuzzy neural classifier method improve classification accuracy.

Breast cancer is one of the illnesses that has become a leading cause of female death worldwide, and early diagnosis of these diseases is a difficult and important undertaking. The intention of this paper is to design prediction models based on Decision Tree, Random Forest and Linear Discriminant Analysis respectively to forecast the breast cancer occurrence at an early stage. The design is focused on analyzing the minimum set of attributes from the clinical data set that have been chosen by experts, and then compare the performances of the models to find their advantages in each model. Breast Cancer Wisconsin Data Set is used in this paper to construct the expected prediction models. Classification accuracy which is determined by comparing real values with predicted values, and generalization ability which is reflected by Receiver Operating Characteristic curve, are used to gauge the performance of the models. The results confirm that the Random Forest model can achieve the highest accuracy to 99.4% and the best generalization ability. However, the Linear Discriminant Analysis model can keep better stability in prediction accuracy and has the fastest running speed.

We aimed to develop a prediction model based on the PIRO concept (Predisposition, Injury, Response and Organ failure) for patients with Hepatitis B Virus (HBV) related acute-on-chronic liver failure (ACLF). 774 patients with HBV related ACLF defined in the CANONIC study were analyzed according to PIRO components. Variables associated with mortality were selected into the prediction model. Based on the regression coefficients, a score for each PIRO component was developed, and a classification and regression tree was used to stratify patients into different nodes. The prediction model was then validated using an independent cohort (n = 155). Factors significantly associated with 90-day mortality were: P: age, gender and ACLF type; I: drug, infection, surgery, and variceal bleeding; R: systemic inflammatory response syndrome (SIRS), spontaneous bacteria peritonitis (SBP), and pneumonia; and O: the CLIF consortium organ failure score (CLIF-C OFs). The areas under the receiver operating characteristics curve (95% confidence interval) for the combined PIRO model for 90-day mortality were 0.77 (0.73–0.80). Based on the scores for each of the PIRO components and the cut-offs estimated from the classification and regression tree, patients were stratified into different nodes with different estimated death probability. Based on the PIRO concept, a new prediction model was developed for patients with HBV related ACLF, allowing stratification into different clusters using the different scores obtained in each PIRO component. The proposed model will likely help to stratify patients at different risk, defining individual management plans, assessing criteria for specific therapies, and predicting outcomes.