From deviation risk to trajectory complexity: machine learning-based deviation characterization and tortuosity analysis for wellbore quality assessment.

Background: Early identification of probable post-traumatic stress disorder (PTSD) can lead to early intervention and treatment.Aims: This study aimed to evaluate supervised machine learning (ML) classifiers for the identification of probable PTSD in those who are serving, or have recently served in the United Kingdom (UK) Armed Forces.Methods: Supervised ML classification techniques were applied to a military cohort of 13,690 serving and ex-serving UK Armed Forces personnel to identify probable PTSD based on self-reported service exposures and a range of validated self-report measures. Data were collected between 2004 and 2009.Results: The predictive performance of supervised ML classifiers to detect cases of probable PTSD were encouraging when compared to a validated measure, demonstrating a capability of supervised ML to detect the cases of probable PTSD. It was possible to identify which variables contributed to the performance, including alcohol misuse, gender and deployment status. A satisfactory sensitivity was obtained across a range of supervised ML classifiers, but sensitivity was low, indicating a potential for false negative diagnoses.Conclusions: Detection of probable PTSD based on self-reported measurement data is feasible, may greatly reduce the burden on public health and improve operational efficiencies by enabling early intervention, before manifestation of symptoms.

Artificial Lift Artificial lift systems are among the most widely used production technologies in global oil and gas operations. Wells that cannot produce liquids to surface under their own pressure require lift technologies to enable production. Some liquid wells need lift assistance from the beginning, and almost all require it sooner or later. Rod pump, electric submersible pump (ESP), and gas lift technologies are the most common methods of providing artificial lift to these wells. Gas wells also require artificial lift methods in most cases to pump liquids to the surface, chiefly water but also oil, so that free gas can flow through the wellbore without impediment. There is no global repository of artificial lift statistics; however, industry observers estimate that 90% to 95% of the world’s producing wells currently use artificial lift, said Bill Lane, vice president of artificial lift systems emerging technologies at Weatherford. “It is trending more toward 95% than 90%, and probably 100% of producing wells would use artificial lift at some point in their lives, except for wells shut in prematurely because of economic factors.” With the ubiquity of artificial lift, “there are so many engineering opportunities in this field,” said Shauna Noonan, wells technology supervisor at ConocoPhillips and SPE technical director for production and operations. Those who embrace those opportunities will face abundant challenges, many of them related to the integration of artificial lift into unconventional horizontal wellbore designs and frontier subsea projects in deep water. Horizontal Well Challenges “Today, our technologies to sweep liquids through the lateral sections of horizontal wells are very limited,” Lane said. “To understand the challenge, note that a 5,000-ft lateral with a cased 4-in. inside diameter has the same slenderness ratio as a ¼-in. drinking straw as long as a football field. Along that straw are numerous undulations, each of which results in a sump in which liquids collect. Wherever this phenomenon occurs, the pressure beyond each sump must increase until the collected liquids are blown further toward the heel of the producing zone. This phenomenon is particularly evident in gassy liquid wells, less so in primary liquid producers.” In a perfect world for artificial lift, “the obvious solution would be toe-up straight lateral sections free from undulations,” Lane said. “But that is easier said than done.” Making wellbores straighter would help from a pumping standpoint. However, other factors usually take precedence. Bores must target the reservoir’s “sweet spots” with the most total organic content. Petrology and geophysics determine where stimulation can be most effective. Well pad designs and lease boundaries can also constrain well placement and design. Mechanical pumps have been deployed into lateral wellbore sections with limited success, experiencing higher than normal failure rates when operating on their sides and encountering problems in keeping gas separate from liquids—a necessity for efficient pump operation. Pumps with single intakes can only pull liquids from one low spot. Thus, locating a pump along a lateral bore with multiple sumps becomes a matter of compromise. For the most part, mechanical pumps are landed in the vertical or near vertical sections of horizontal wells, and are typically not landed beyond 60° deviation.

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticles (LNP) for mRNA delivery to hyperactivated microglia, particularly those with pro-inflammatory characteristics, utilizing supervised machine learning (ML) classifiers. We developed and screened a library of 216 LNP formulations with varying lipid compositions, N/P ratios, and hyaluronic acid (HA) modifications. The transfection efficiency of eGFP mRNA was assessed in the BV-2 murine microglia cell line under different immunological states, including resting and activated conditions (LPS-activated and IL4/IL13-activated). ML-guided morphometric analysis tracked the phenotypes of various microglia subtypes before and after transfection. Four supervised ML classifiers were investigated to predict transfection efficiency and phenotypic changes based on LNP design parameters. The Multi-Layer Perceptron (MLP) neural network emerged as the best-performing model, achieving weighted F1-scores ≥0.8. While it accurately predicted responses from LPS-activated and resting cells, it struggled with IL4/IL13-activated cells. The MLP model was validated by predicting the performance of four unseen LNP formulations delivering eGFP mRNA to LPS-activated BV2 cells. HA-LNP2 emerged as optimal formulation for delivering target IL10 mRNA, effectively suppressing inflammatory phenotypes, evidenced by shifts in cell morphology, increased IL10 expression, and reduced TNF-α levels. We also evaluated HA-LNP2 on LPS-activated human iPSC-derived microglia, confirming its efficacy in modulating inflammatory responses. This study highlights the potential of tailored LNP design and ML techniques to enhance mRNA therapy for neuroinflammatory disorders by leveraging carrier’s immunogenic properties to modulate microglial responses.

Context: Primary aldosteronism (PA) is a common cause of secondary hypertension and is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who can be cured by unilateral adrenalectomy and should be referred to specialized centers. The use of machine learning has been introduced in some fields of clinical prediction. Combining machine learning with clinical data could lead to the development of new models for predicting unilateral subtype of PA. Objective: The aim of this study was to develop a predictive model of subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Design and setting: This was a retrospective cross-sectional study in referral centers. Patients: We retrospectively analyzed 91 patients with unilateral and 138 patients with bilateral subtype of PA diagnosed according to adrenal venous sampling findings, and stratified randomly split to the training (80%) and test cohorts (20%). Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop prediction models from 21 clinical variables. Classifiers were trained using stratified 10 fold cross-validation of the training cohort and hyperparameters of each classifier were adjusted using grid search to optimize the accuracy in the training cohort. Main Outcome Measures: The accuracy and the area under the receiver operating characteristic curve (AUC) for subtype prediction in the test cohort were compared among the optimized classifiers. Results: Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium levels, plasma aldosterone concentrations, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF. Conclusions: Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.

The geomechanical properties of the Silah field were measured to identify the various types of rocks in the area and to determine the economically valuable zones with good petrophysical properties. A suite of well logging data was interpreted to delineate the subsurface lithostratigraphic units and permeable reservoirs across the two drilled wells. The estimated geomechanical parameters (compressional sonic velocity, shear sonic velocity, acoustic impedance, and Poisson's ratio) and certain petrophysical parameters (porosity and shale volume) were computed using empirical relations. This study aims to characterize and evaluate the sedimentary rock units of the field to delineate potential hydrocarbon reservoir formations based on their geomechanical and petrophysical properties. The well logging data interpretation and geomechanical properties analysis reveal that the subsurface lithostratigraphy comprises four types of lithology: sand, carbonate (limestone and dolomite), siltstone, and shale, interbedded. The main interesting reservoirs are in the Kharita, Bahariya, and Abu Roash (G and F members). The different constructed geomechanical crossplots indicate that the main lithology consists of shaley sandstone to sandstone and carbonates, with the shale type varying from predominantly laminated to partly dispersed. The results show that Larionov shale volume values (9.6% to 32%), total porosity ranged 10% to 26%, the compressional to shear wave velocity ratio (Vp/Vs) ranges from 1.58 to 1.912, and Poisson’s ratio values vary from 0.171 to 0.306. Also, the effective porosity ranges from 3.7%- 19.3%. There is a substantial bed of shale beneath and above these zones, which serves as a possible source and/or seal rock.

We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive. Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.

Does Multiple Criteria Decision Aiding (MCDA) improve the process of evaluating Machine Learning (ML) algorithms, when critical criteria of fairness are concurrently considered, beyond predictive power? To address this question, we employ several notions of fairness, such as Demographic Parity, Equalized Odds, and Lack of Disparate Mistreatment, and we appraise a set of supervised ML classifiers, under one of the most popular MCDA outranking methods, that is, the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) II. Moreover, to avoid any arbitrary choice in the importance attached to the criteria we apply the Stochastic Multicriteria Acceptability Analysis (SMAA), providing information in statistical terms through simulations. The empirical testing is processed over well-known databases, with several representative sub-datasets, securing variation in terms of observations’ volume. Overall, a series of ranking patterns that persists in the evaluation of the ML classifiers, across the utilized MCDA methodology and datasets, offers valuable relevant insights and documents specific useful interpretations. The obtained findings provide robust support that MCDA can be effectively exploited for the appraisal of ML classifiers, when aiming at the simultaneous consideration of critical fairness metrics, apart from the typical dimensions related to predictive power.

Malware Detection using conventional methods is incompetent to detect new and generic malware. For the investigation of a variety of malware, there were no ready-made machine learning datasets available for malware detection. So we generated our dataset by downloading a variety of malware files from the world's famous malware projects. By performing unstructured data collection from the downloaded APK files and feature mining process the final dataset was generated with 16300 records and a total of 215 features. There was a need to evaluate the performance of the generated dataset with supervised machine learning classifiers. So in this paper, we propose a malware detection approach using different supervised machine learning classifiers. Here supervised algorithms, Feature Reduction Techniques, and Ensembling techniques are used to evaluate the performance of the generated dataset. Machine Learning classifiers are evaluated on the evaluation parameters like AUC, FPR, TPR, Cohen Kappa Score, Precision, and Accuracy. We also represented the results of classifiers using Bar plots of Accuracy and plotting the ROC curve. From the results of machine learning classifiers, the performance of the CatBoost Classifier is highest with Accuracy 93.15% having a value of ROC curve as 0.91 and Cohen Kappa Score as 81.56%.

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

_ For decades, artificial lift optimization has relied heavily on manual intervention, periodic well tests, and the judgment of experienced engineers. Electric submersible pumps (ESPs), gas lift, and beam pumps (the three most widely used lift systems) each have presented their own optimization challenges, such as balancing set points, managing compressor constraints, and interpreting dynamometer cards. These processes, while effective, have historically been time-intensive, reactive, and difficult to scale across large well inventories. The accelerating deployment of machine learning (ML) and automation is changing this landscape. By embedding intelligence into the control loop, operators now can move from reactive decision-making to proactive, continuous optimization. What was once a manual task handled by individual engineers is increasingly performed by autonomous systems that learn from data, adjust in real time, and scale effortlessly across hundreds or thousands of wells. Recent case studies illustrate how this transformation is unfolding across different lift types, each demonstrating that the age of autonomy in artificial lift is no longer aspirational but is now an operational reality. Neural-network models trained on ESP data sets in the Permian Basin now recommend and directly write optimal pump set points, as shown in paper SPE 219528. The deployment delivered a 2–4% oil uplift and longer run life, while demonstrating full self-pumping capability, where pumps operate with minimal human oversight. In gas lift, ExxonMobil’s large-scale rollout of a closed-loop optimization workflow has been equally impactful. As detailed in paper SPE 219553, more than 1,300 wells are now managed by an automated system that runs multirate tests, updates downhole pressure models, and applies ML for wells without gauges. The outcome: a consistent approximately 2% production uplift, achieved with little incremental operational or capital expenditure. On the beam-pump side, manual interpretation of dynamometer cards is being replaced by real-time ML classification. In paper SPE 224979, more than 700,000 dynacards were analyzed with models such as XGBoost and convolutional neural networks, achieving approximately 95% classification accuracy. By integrating with OSIsoft AF and exception-based surveillance, the solution provides predictive analytics and proactive alerts, reducing downtime and extending equipment life. When comparing the three approaches, some clear distinctions emerge. In ESP optimization (SPE 219528), operators relied on neural networks to recommend and autonomously adjust set points, achieving production uplift across more than 200 wells. Gas lift optimization (SPE 219553) took a broader fieldwide approach, scaling a closed-loop workflow to more than 1,300 wells, where multirate testing and ML models continuously updated injection rates, delivering uplift at minimal incremental cost. Meanwhile, beam-pump optimization (SPE 224979) focused less on direct production uplift and more on reliability and predictive surveillance, where ML models enabled proactive maintenance and reduced downtime. Despite their differences, the three initiatives share commonalities: Each system moved from manual, engineer-driven adjustments to automated and autonomous optimization; each leveraged ML in combination with existing field infrastructure; and each demonstrated that even modest per-well improvements, when scaled across hundreds or thousands of wells, translate into substantial fieldwide gains. Across ESPs, gas lift, and beam pumps, these case studies demonstrate a unifying shift: ML is redefining artificial lift as a self-optimizing, continuously learning system. Production uplifts of 2–4%, when multiplied across fields, translate into substantial incremental barrels. Equipment longevity improves as systems learn to operate within optimal ranges, while engineers are freed from repetitive monitoring to focus on higher-value analysis. The advances described here are more than isolated technical improvements; they are signposts of a broader digital transformation across upstream operations. Artificial lift, long the domain of manual adjustments and engineer intuition, is now becoming a proving ground for autonomy. By embedding AI into the very core of production systems, operators are not just optimizing individual wells; they are building the foundation of the digital oilfield of the future, where decisions are data-driven, workflows are autonomous, and engineers focus their expertise on complex challenges rather than routine tuning. Summarized Papers in This October 2025 Issue SPE 219528 - Machine Learning Optimizes Autonomous ESP Use in the Permian by Ryan D. Erickson, Vital Energy, et al. SPE 219553 - Gas Lift Optimization in the Permian Uses Machine Learning, Artificial Intelligence by Pooya Movahed, ExxonMobil, et al. SPE 224979 - Real-Time Machine Learning Enhances Dynacard Surveillance, Predictive Analytics by Shifa Khaliah Al Kiyumi, Daleel Petroleum, et al.

Artificial intelligence (AI) is a tool thought to revolutionize the field of reproductive medicine in the years to come. Specifically, machine learning (ML), which is a subset of AI methods used to detect patterns and make predictions based on large datasets, has been used in ART to predict implantation outcome, embryo transfer strategies or adverse outcomes for IVF treatments (for a review see, Wang et al., 2019). However, ML methods have not yet been widely adopted in medically assisted reproduction (MAR). This slow uptake could be due to a lack of in-depth interdisciplinary communication between ML experts and clinicians/embryologists, as well as some uncertainty on how ML models could be generalized for different populations. The February edition of the ESHRE Journal Club discussed a paper from Yland et al. (2022) where the authors compared different ML algorithms to predict the chance of pregnancy in couples actively trying to conceive (without undergoing MAR). By using data from the Pregnancy Study Online (PRESTO), a web-based prospective cohort study of 4133 couples in North America (Wise et al., 2015), the authors analysed 163 potential variables and used supervised ML classification algorithms to identify the strength of each variable in predicting pregnancy outcomes of couples across the fertility spectrum (infertile: <12 menstrual cycles; subfertile: within 6 menstrual cycles; and fecundability: the average probability of pregnancy per menstrual cycle). The authors were able to predict pregnancy with a discrimination as high as 71.2% and identify the variables that most consistently predicted conception. These variables included lifestyle and reproductive characteristics such as age, BMI, history of infertility, daily use of vitamins or folic acid, intercourse timing, diet quality and reduced stress. ESHRE Journal Club discussion focused on promoting interdisciplinarity among ML and reproductive medicine specialists. It hosted 50 participants on Twitter together with experts Michelle Perugini, Vajira Thambawita and authors Jennifer Yland, Yannis Paschalidis and Lauren Wise. The discussion resulted in 902 tweets and around 1 million impressions over a 24-h period.

In the Oil and Gas industry, there is a constant look for time and cost savings through performance enhancement and risk reduction. Not less important, wellbore quality becomes a crucial factor across target production intervals which enable safe and optimum completion operations in the well. While the techniques to drill wells constantly evolve, technology is advancing at faster pace every year. The application of new tools and digital technologies is the step change from progessive growth to exponential increase in performance. This paper contains a detailed description of a successful implementation of a combined integrated strategy, including the procedures established to maximize both; performance and wellbore quality in highly deviated and lateral horizontal sections in deep gas wells in a giant gas field in the Middle East. It describes the application of specific technologies that helped to improve wellbore quality and allowed corrections in Real Time.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

Freshwater ecosystems play a crucial role in maintaining ecological balance and supporting biodiversity. Assessing water quality and identifying pollution factors affected by seasonal changes is of utmost importance. The traditional approach for identifying pollution sources and seasonal water quality assessment is often labor-intensive, time-consuming, and complex. In response, our study introduces a hybrid receptor, multivariate statistical and machine learning (ML) based framework as an alternative. Positive matrix factorization (PMF) and receptor model were used to identify key pollutant sources, while Principal Component Analysis (PCA) was used to corroborate pollutant sources. Additionally, various supervised ML classification algorithms are used to predict and identify pollution sources on full physicochemical datasets and reduced in situ parameter scenarios. ML model stability and generalization capacity were evaluated using learning curves, cross-validation method. The findings indicate significant seasonal variability in water quality index, ranging from 38.5 (poor) to 78.6 (good). PMF and PCA revealed three dominant pollution factors, such as biogeochemical processes, sediment resuspension/nutrient influx, and anthropogenic pollution, affecting the seasonal water quality by 15-30%, 25-45%, and 40-70%, respectively. Model performance indicators using confusion matrices, ROC-AUC, and other evaluation metrics, for full dataset identified XGBoost as the best model, achieving the highest accuracy of 96.3%, followed by CatBoosting (93.6%), Random Forest (93.6%), Support Vector Classifier (91.8%), K-Nearest Neighbors (87.2%) and Decision Tree (84.3%). However, with the in situ data the ML models show stable and consistent classification accuracy in the range of approximately 70-81%. Overall, the modelling framework provides an effective approach for capturing seasonal variability and pollution source attribution, contributing to improved ecological understanding and predictive management of freshwater system.

Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.