Predicting Long-Term Outcome After Traumatic Brain Injury Using Repeated Measurements of Glasgow Coma Scale and Data Mining Methods

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.

Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review

STUDY OBJECTIVE

Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research.

METHODS

We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers.

RESULTS

Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed.

CONCLUSION

This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.

Clinical Characteristics and Outcomes of Acute Childhood Encephalopathy in a Tertiary Pediatric Intensive Care Unit

AIM

Childhood encephalopathy comprises a wide range of etiologies with distinctive distribution in different age groups. We reviewed the pattern of encephalopathy admitted to the pediatric intensive care unit (PICU) of a tertiary children's hospital.

METHODS

We reviewed the medical records and reported the etiologies, clinical features, and outcomes of children with encephalopathy.

RESULTS

Twenty-four admissions to the PICU between April 2019 and May 2020 were reviewed. The median (interquartile range) age was 10.0 (14.7) years and 62.5% were boys. Confusion (66.7%) was the most common presentation. Adverse effects related to medications (33.3%) and metabolic disease (20.8%) were predominant causes of encephalopathies in our study cohort. Methotrexate was responsible for most of the medication-associated encephalopathy (37.5%), whereas Leigh syndrome, pyruvate dehydrogenase deficiency and Wernicke's encephalopathy accounted for those with metabolic disease. The median Glasgow Coma Scale (GCS) on admission was 12.5 (9.0). Antimicrobials (95.8%) and antiepileptic drugs (60.9%) were the most frequently given treatment. Children aged 2 years or younger were all boys (P = 0.022) and had a higher proportion of primary metabolic disease (P = 0.04). Intoxication or drug reaction only occurred in older children. The mortality was 8.3%, and over half of the survivors had residual neurological disability upon PICU discharge. Primary metabolic disease (P = 0.002), mechanical ventilation (P = 0.019), failure to regain GCS back to baseline level (P = 0.009), and abnormal cognitive function on admission (P = 0.03) were associated with cerebral function impairment on PICU discharge.

CONCLUSIONS

Primary metabolic encephalopathy was prevalent in younger children, whereas drug-induced toxic encephalopathy was common among older oncology patients. Survivors have significant neurologic morbidity. Failure to regain baseline GCS was a poor prognostic factor for neurological outcomes.

Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury

Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.

Which criteria is a better predictor of ICU admission in trauma patients? An artificial neural network approach

PURPOSE

One of the most critical concerns in the intensive care unit (ICU) section is identifying the best criteria for entering patients to this part. This study aimed to predict the best compatible criteria for entering trauma patients in the ICU section.

METHOD

The present study was a historical cohort study. The data were collected from 2448 trauma patients referring to Shahid Rajaee Hospital between January 2015 and January 2017 in Shiraz, Iran. The artificial neural network (ANN) models with cross-validation and logistic regression (LR) with a backward method was used for data analysis. The final analysis was performed on a total of 958 patients who were transferred to the ICU section.

RESULTS

Based on the present results, the motor component of the GCS score at each cutoff point had the highest importance. The results also showed better performance for the AUC and accuracy rate for ANN compared with LR.

CONCLUSION

The most critical indicators in predicting the optimal use of ICU services in this study were the Motor component of the GCS. Results revealed that the ANN had a better performance than the LR in predicting the main outcomes of the traumatic patients in both the accuracy and AUC index. Trauma section surgeons and ICU specialists will benefit from this study's results and can assist them in making decisions to predict the patient outcomes before entering the ICU.

The detection of mild traumatic brain injury in paediatrics using artificial neural networks

Head computed tomography (CT) is the gold standard in emergency departments (EDs) to evaluate mild traumatic brain injury (mTBI) patients, especially for paediatrics. Data-driven models for successfully classifying head CT scans that have mTBI will be valuable in terms of timeliness and cost-effectiveness for TBI diagnosis. This study applied two different machine learning (ML) models to diagnose mTBI in a paediatric population collected as part of the paediatric emergency care applied research network (PECARN) study between 2004 and 2006. The models were conducted using 15,271 patients under the age of 18 years with mTBI and had a head CT report. In the conventional model, random forest (RF) ranked the features to reduce data dimensionality and the top ranked features were used to train a shallow artificial neural network (ANN) model. In the second model, a deep ANN applied to classify positive and negative mTBI patients using the entirety of the features available. The dataset was divided into two subsets: 80% for training and 20% for testing using five-fold cross-validation. Accuracy, sensitivity, precision, and specificity were calculated by comparing the model's prediction outcome to the actual diagnosis for each patient. RF ranked ten clinical demographic features and twelve CT-findings; the hybrid RF-ANN model achieved an average specificity of 99.96%, sensitivity of 95.98%, precision of 99.25%, and accuracy of 99.74% in identifying positive mTBI from negative mTBI subjects. The deep ANN proved its ability to carry out the task efficiently with an average specificity of 99.9%, sensitivity of 99.2%, precision of 99.9%, and accuracy of 99.9%. The performance of the two proposed models demonstrated the feasibility of using ANN to diagnose mTBI in a paediatric population. This is the first study to investigate deep ANN in a paediatric cohort with mTBI using clinical and non-imaging data and diagnose mTBI with balanced sensitivity and specificity using shallow and deep ML models. This method, if validated, would have the potential to reduce the burden of TBI evaluation in EDs and aide clinicians in the decision-making process.

Deletion of ferritin H in neurons counteracts the protective effect of melatonin against traumatic brain injury-induced ferroptosis

Accumulating evidence demonstrates that ferroptosis may be important in the pathophysiological process of traumatic brain injury (TBI). As a major hormone of the pineal gland, melatonin exerts many beneficial effects on TBI, but there is no information regarding the effects of melatonin on ferroptosis after TBI. As expected, TBI resulted in the time-course changes of ferroptosis-related molecules expression and iron accumulation in the ipsilateral cortex. Importantly, we found that treating with melatonin potently rescued TBI induced the changes mentioned above and improved functional deficits versus vehicle. Similar results were obtained with a ferroptosis inhibitor, liproxstatin-1. Moreover, the protective effect of melatonin is likely dependent on melatonin receptor 1B (MT2). Although ferritin plays a vital role in iron metabolism by storing excess cellular iron, its precise function in the brain, and whether it involves melatonin's neuroprotection remain unexplored. Considering ferritin H (Fth) is expressed predominantly in the neurons and global loss of Fth in mice induces early embryonic lethality, we then generated neuron-specific Fth conditional knockout (Fth-KO) mice, which are viable and fertile but have altered iron metabolism. In addition, Fth-KO mice were more susceptible to ferroptosis after TBI, and the neuroprotection by melatonin was largely abolished in Fth-KO mice. In vitro siFth experiments further confirmed the results mentioned above. Taken together, these data indicate that melatonin produces cerebroprotection, at least partly by inhibiting neuronal Fth-mediated ferroptosis following TBI, supporting the notion that melatonin is an excellent ferroptosis inhibitor and its anti-ferroptosis provides a potential therapeutic target for treating TBI.

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

BACKGROUND

Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers.

METHODS

MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method.

RESULTS

The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change.

CONCLUSIONS

The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury

Gold standard prognostic models for long-term outcome in patients with severe traumatic brain injury (TBI) use admission characteristics and are considered useful in some areas but not for clinical practice. In this study, we aimed to build prognostic models for 6-month Glasgow Outcome Score (GOS) in patients with severe TBI, combining baseline characteristics with physiological, treatment, and injury severity data collected during the first 24 h after injury. We used a training dataset of 472 TBI subjects and several data mining algorithms to predict the long-term neurological outcome. Performance of these algorithms was assessed in an independent (test) sample of 158 subjects. The least absolute shrinkage and selection operator (LASSO) led to the highest prediction accuracy (area under the receiving operating characteristic curve = 0.86) in the test set. The most important post-baseline predictor of GOS was the best motor Glasgow Coma Scale (GCS) recorded in the first day post-injury. The LASSO model containing the best motor GCS and baseline variables as predictors outperformed a model with baseline data only. TBI patient physiology of the first day-post-injury did not have a major contribution to patient prognosis six months after injury. In conclusion, 6-month GOS in patients with TBI can be predicted with good accuracy by the end of the first day post-injury, using hospital admission data and information on the best motor GCS achieved during those first 24 h post-injury. Passed the first day after injury, important physiological predictors could emerge from landmark analyses, leading to prediction models of higher accuracy than the one proposed in the current research.

Key Hospital Anxiety and Depression Scale (HADS) items associated with DSM-IV depressive and anxiety disorder 12-months post traumatic brain injury

BACKGROUND

Anxiety and depression are common problems following traumatic brain injury (TBI), warranting routine screening. Self-report rating scales including the Hospital Anxiety and Depression Scale (HADS) are associated with depression and anxiety diagnoses in individuals with TBI. The relationship between individual HADS symptoms and structured clinical interview methods (SCID) requires further investigation, particularly in regard to identifying a small number of key items that can potentially be recognised by clinicians and carers of individuals with TBI.

METHODS

138 individuals sustaining a complicated-mild to severe TBI completed the HADS, and the Structured Clinical Interview for DSM-IV, Research Version (SCID) at 12-months post-injury. The associations between individual HADS items, separately and in combination, as well as overall depression and anxiety subscale scores, and SCID-diagnosed depressive and anxiety disorders were analysed.

RESULTS

CART (Classification and Regression Tree) analysis found HADS depression item 2 "I still enjoy the things I used to enjoy" and a combination of two anxiety items, 3 "I get a sort of frightened feeling as if something awful is about to happen" and 5 "worrying thoughts go through my mind", performed similarly to total depression and anxiety subscales in terms of their association with depressive and anxiety disorders respectively, at 12-months post-injury.

LIMITATIONS

Patients were predominantly injured in motor vehicle accidents and received comprehensive care within a no-fault accident compensation system and so may not be representative of the wider TBI population.

CONCLUSIONS

Although validation is required, a small number of self-report items are highly associated with 12-month post-injury diagnoses.

Use of Diffusional Kurtosis Imaging and Dynamic Contrast-Enhanced MR Imaging to Predict Posttraumatic Epilepsy in Rabbits

BACKGROUND AND PURPOSE

Finding a reliable biomarker to thoroughly assess the brain structure changes in posttraumatic epilepsy is of great importance. Our aim was to explore the value of diffusional kurtosis imaging combined with dynamic contrast-enhanced MR imaging in the evaluation of posttraumatic epilepsy.

MATERIALS AND METHODS

A modified weight-drop device was used to induce traumatic brain injury. Rabbits were exposed to traumatic brain injury or sham injury. Diffusional kurtosis imaging and dynamic contrast-enhanced MR imaging were performed 1 day after injury. Posttraumatic epilepsy was investigated 3 months after injury. The traumatic brain injury group was further divided into 2 groups: the posttraumatic epilepsy and the non-posttraumatic epilepsy groups. Mean kurtosis and volume transfer coefficient values in the cortex, hippocampus, and thalamus were analyzed. After follow-up, the experimental animals were sacrificed for Nissl staining.

RESULTS

The posttraumatic epilepsy group comprised 8 rabbits. In the ipsilateral cortex, the volume transfer coefficient in the traumatic brain injury group was higher than that in the sham group; the volume transfer coefficient in the posttraumatic epilepsy group was higher than that in the non-posttraumatic epilepsy group. In the ipsilateral hippocampus, the volume transfer coefficient in the posttraumatic epilepsy group was higher than that in the non-posttraumatic epilepsy and sham groups. No difference was observed between the non-posttraumatic epilepsy and sham groups. In the ipsilateral cortex, mean kurtosis in the traumatic brain injury group was lower than that in the sham group, and mean kurtosis in the posttraumatic epilepsy group was lower than that in the non-posttraumatic epilepsy group. In the ipsilateral thalamus and hippocampus, mean kurtosis in the traumatic brain injury group was lower than that in the sham group, and mean kurtosis in the posttraumatic epilepsy group was lower than that in the non-posttraumatic epilepsy group. In the contralateral thalamus, mean kurtosis in the traumatic brain injury group was lower than that in the sham group; however, no difference was observed between the posttraumatic epilepsy and non-posttraumatic epilepsy groups.

CONCLUSIONS

Diffusional kurtosis imaging and dynamic contrast-enhanced MR imaging could be used to predict the occurrence of posttraumatic epilepsy in rabbits exposed to experimental traumatic brain injury.

Bayesian averaging over decision tree models: An application for estimating uncertainty in trauma severity scoring

INTRODUCTION

For making reliable decisions, practitioners need to estimate uncertainties that exist in data and decision models. In this paper we analyse uncertainties of predicting survival probability for patients in trauma care. The existing prediction methodology employs logistic regression modelling of Trauma and Injury Severity Score (TRISS), which is based on theoretical assumptions. These assumptions limit the capability of TRISS methodology to provide accurate and reliable predictions.

METHODS

We adopt the methodology of Bayesian model averaging and show how this methodology can be applied to decision trees in order to provide practitioners with new insights into the uncertainty. The proposed method has been validated on a large set of 447,176 cases registered in the US National Trauma Data Bank in terms of discrimination ability evaluated with receiver operating characteristic (ROC) and precision-recall (PRC) curves.

RESULTS

Areas under curves were improved for ROC from 0.951 to 0.956 (p = 3.89 × 10^-18) and for PRC from 0.564 to 0.605 (p = 3.89 × 10^-18). The new model has significantly better calibration in terms of the Hosmer-Lemeshow Hˆ statistic, showing an improvement from 223.14 (the standard method) to 11.59 (p = 2.31 × 10^-18).

CONCLUSION

The proposed Bayesian method is capable of improving the accuracy and reliability of survival prediction. The new method has been made available for evaluation purposes as a web application.

The influence of post-acute rehabilitation length of stay on traumatic brain injury outcome: a retrospective exploratory study

OBJECTIVE

Data regarding length of stay (LOS) in a rehabilitation programme after traumatic brain injury (TBI) are limited. The goal of this study was to examine the effect of LOS and disability on outcome following TBI.

METHODS

Records from patients in a multidisciplinary rehabilitation programme at least 3 months after TBI were analysed retrospectively to study the influence of LOS on functional outcome at different levels of disability. Functional status was determined by the Mayo-Portland Adaptability Inventory (MPAI) and the Community Integration Questionnaire (CIQ). Patients were further grouped by time since injury of 3-12 months or over 1 year.

RESULTS

Those with a mild and moderate disabilities and over 1 year chronicity showed improvements after 90 days of rehabilitation. Patients with a severe disability and over 1 year chronicity required at least 180 days to show improvements. Moderately and severely disabled patients with an injury chronicity of 3-12 months showed improvements in the MPAI after 90 days. However, further improvement was observed after 180 days in the severely disabled group.

CONCLUSIONS

Results suggest that both, level of disability and injury chronicity, should be considered when determining LOS. Data also show an association between LOS and changes in the MPAI and CIQ.

Accurate and dynamic predictive model for better prediction in medicine and healthcare

INTRODUCTION

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance.

AIMS AND OBJECTIVES

In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.

CONCLUSION

The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm

An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate the mortality risk of patients undergoing surgery is complex due to the variables associated. The use of clinical decision support systems based on machine learning could help medical staff to improve the results of surgery and get a better understanding of the disease. In this work, the authors present a predictive system of inhospital mortality in patients who were undergoing to open repair of abdominal aortic aneurysm. Different methods as multilayer perceptron, radial basis function and Bayesian networks are used. Results are measured in terms of accuracy, sensitivity and specificity of the classifiers, achieving an accuracy higher than 95%. The developing of a system based on the algorithms tested can be useful for medical staff in order to make a better planning of care and reducing undesirable surgery results and the cost of the post-surgical treatments.

Diagnosis of Brain Metastases from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm

Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis tend to have a poor survivability, which is less than 6 months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the patients' survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) as the local search technique. The proposed method is applied to 44 UCI and IDA datasets, and 5 cancers microarray datasets as preliminary experiment. In addition, this method is tested on 4 lung cancer microarray public dataset. Further, we tested our method on a nationwide dataset of brain metastasis from lung cancer (BMLC) in Taiwan. Since the nature of real medical dataset to be highly imbalanced, the synthetic minority over-sampling technique (SMOTE) is utilized to handle this problem. The proposed method is compared against another 8 popular benchmark classifiers and feature selection methods. The performance evaluation is based on the accuracy and Kappa index. For the 44 UCI and IDA datasets and 5 cancer microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 lung cancer public microarray datasets, MEM-SVM still achieved the highest mean value for accuracy and Kappa index. Due to the imbalanced property on the real case of BMLC dataset, all methods achieve good accuracy without significance difference among the methods. However, on the balanced BMLC dataset, MEM-SVM appears to be the best method with higher accuracy and Kappa index. We successfully developed MEM-SVM to predict the occurrence of brain metastasis from lung cancer with the combination of SMOTE technique to handle the class imbalance properties. The results confirmed that MEM-SVM has good diagnosis power and can be applied as an alternative diagnosis tool in with other medical tests for the early detection of brain metastasis from lung cancer.