Non-contrast CT in the Evaluation of Urinary Tract Stone Obstruction and Haematuria

Non-contrast computed tomography (CT) abdomen has emerged as a first line investigation in suspected upper urinary tract obstruction. Underlying causes can usually be ascertained on computed tomography of kidneys, ureters and bladder (CT KUB). However, further investigations may be required to delineate/confirm underlying pathology like ureteropelvic junction obstruction (UPJ), differentiation between obstruction and residual dilatation. Actual protocol of CT KUB for evaluation of stone disease and haematuria vary on institutional guidelines. CT KUB is not only extremely sensitive and specific in the diagnosis of stone; it is now used in the pre-operative nomograms in predicting success of various endourological interventions like percutaneous nephrolithotomy (PCNL) and shock wave lithotripsy (SWL). Determination of stone density, stone volume, stone composition, skin to stone distance, presence of ureteral wall oedema, perinephric oedema are highly predictive of stone free rate. CT recognition of various anomalies, presence of retro-renal colon, horse-shoe kidney, malrotation, etc. can help in better planning to avoid complications. One of the major limitations of CT is the radiation dose, besides cost and availability. Modification in technique and technological innovation has resulted in significant dose reduction from 4.5 to about 1 mSv

A numerical method for frequent pattern mining

Frequent pattern mining is one of the active research themes in data mining. It plays an important role in all data mining tasks such as clustering, classification, prediction, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of patterns generated. A reasonable solution is identifying maximal frequent patterns which form the smallest representative set of patterns to generate all frequent patterns. In this paper, an efficient numerical method for mining frequent patterns is proposed. This method is based on prime number characteristics to generate all frequent patterns by using maximal frequent ones. There are two new properties introduced in this method; a novel tree structure called PC_Tree and PC_Miner algorithm. The PC_Tree is a simple tree structure but yet capable to capture the whole of transactions information with an efficient data transformation technique that utilizes the prime number theory. The PC_Miner algorithm traverses the PC_Tree by using an efficient pruning technique. The experimental results verify the compactness and the efficiency of mining shown by the proposed method

Lymphoepithelioma-like carcinoma of urinary bladder: (LELCA)

Lymphoepithelioma-like carcinoma of the bladder (LELCA) is an uncommon neoplasm of the urinary bladder and up till now only 49 cases have been reported in the English literature. It is imperative to distinguish between lymphoepithelioma-like carcinoma and malignant lymphoma as primary bladder lymphoma is extremely rare. We report a case of a 55 year old lady who presented with the complaint of burning micturition and gross hematuria for the past 5 months. There were no other known comorbids. Pelvic ultrasound was normal. Cystoscopy showed a 4x4 cm sessile mass in the bladder. Histopathological examination was consistent with the diagnosis of lymphoepithelioma like carcinoma of the urinary bladder

OAERP: a better measure than accuracy in discriminating a better solution for stochastic classification training

The use of accuracy metric for stochastic classification training could lead the solution selecting towards the sub-optimal solution due to its less distinctive value and also unable to perform optimally when confronted with imbalanced class problem. In this study, a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects was proposed. This new evaluation metric is known as Optimized Accuracy with Extended Recall-precision (OAERP). By using two examples, the results has shown that the OAERP metric has produced more distinctive and discriminating values as compared to accuracy metric. This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. In addition, the OAERP metric also performed effectively when dealing with imbalanced class problems. Moreover, the t-test analysis also shows a clear advantage of the MCS model trained by the OAERP metric against its previous metric over five out of nine medical data sets. From the abovementioned results, it is clearly indicates that the OAERP metric is more likely to choose a better solution during classification training and lead towards a better trained classification model

A new method for mining maximal frequent itemsets

In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces an efficient database encoding technique, a novel tree structure called PC_Tree and also PC_Miner algorithm. The database encoding technique utilizes Prime number characteristics and transforms each transaction into a positive integer that has all properties of its items. The PC_Tree is a simple tree structure but yet powerful to capture whole of transactions by one database scan. The PC_Miner algorithm traverses the PC_Tree and builds the gcd (greatest common divisor) set of its nodes to mine maximal frequent itemsets. Experiments verify the efficiency and advantages of the proposed method

Efficient prime-based method for interactive mining of frequent patterns.

Over the past decade, an increasing number of efficient algorithms have been proposed to mine frequent patterns by satisfying the minimum support threshold. Generally, determining an appropriate value for minimum support threshold is extremely difficult. This is because the appropriate value depends on the type of application and expectation of the user. Moreover, in some real-time applications such as web mining and e-business, finding new correlations between patterns by changing the minimum support threshold is needed. Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. Recently, a few efficient interactive mining algorithms have been proposed, which are able to capture the content of transaction database to eliminate possibility of the database rescanning. In this paper, we propose a new method based on prime number and its characteristics mainly for interactive mining of frequent patterns. Our method isolates the mining model from the mining process such that once the mining model is constructed; it can be frequently used by mining process with various minimum support thresholds. During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. The experimental results verify the efficiency of our method for interactive mining of frequent patterns

PC Tree: Prime-Based and Compressed Tree for Maximal Frequent Patterns Mining

Abstract Knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is an essential step in knowledge discovery process. Frequent patterns play an important role in data mining tasks such as clustering, classification, and prediction and association analysis. However, the mining of all frequent patterns will lead to a massive number of patterns. A reasonable solution is identifying maximal frequent patterns which form the smallest representative set of patterns to generate all frequent patterns. This research proposes a new method for mining maximal frequent patterns. The method includes an efficient database encoding technique, a novel tree structure called PC Tree and PC Miner algorithm. Experiment results verify the compactness and performance

Multi-label classification for physical activity recognition from various accelerometer sensor positions

In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated.In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities.Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately.Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue.The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time

A hybrid evaluation metric for optimizing classifier

The accuracy metric has been widely used for discriminating and selecting an optimal solution in constructing an optimized classifier. However, the use of accuracy metric leads the searching process to the sub-optimal solutions due to its limited capability of discriminating values. In this study, we propose a hybrid evaluation metric, which combines the accuracy metric with the precision and recall metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric using two counter-examples. To verify this advantage, we conduct an empirical verification using a statistical discriminative analysis to prove that the OARP is statistically more discriminating than the accuracy metric. We also empirically demonstrate that a naive stochastic classification algorithm trained with the OARP metric is able to obtain better predictive results than the one trained with the conventional accuracy metric. The experiments have proved that the OARP metric is a better evaluator and optimizer in the constructing of optimized classifier