Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network

Evaluate Various Techniques of Data Warehouse and Data Mining with Web Based Tool

All enterprise has a crucial role to play proficiently and productively to maintain its survival in the market and increase its profitability shares. This challenge becomes more complicated with advancement in information technology along with increasing volume and complexity of information. Currently, success of an enterprise is not just the result of efforts by resources but also depends upon its ability to mine the data from the stored information. Data warehousing is a compilation of decision making procedure to integrate and manage the large variant data efficiently and scientifically. Data mining shores up organizations, scrutinize their data more effectively and proficiently to achieve valuable information, that can reward an intelligent and strategic decision making. Data mining has several techniques and maths algorithms which are used to mine large data to increase the organization performance and strategic decision-making. Clustering is a powerful and widely accepted data mining method used to segregate the large data sets into group of similar objects and provides to the end user a sophisticated view of database. This study discusses the basic concept of clustering; its meaning and applications, especially in business for division and selection of target market. This technique is useful in marketing or sales side and, for example, sends a promotion to the right target for that product or service. Association is a known data mining techniques. A pattern is inferred based on an affiliation between matter of same business transaction. It is also referred as relation technique. Large enterprises depend on this technique to research customer's buying preferences. For instance, to track people's buying behavior, retailers might categorize that a customer always buy sambar onion when they buy dal, and therefore suggest that the next time that they buy dal they might also want to buy onion. Classification – it is one of the data mining concept differs from the above in a way it is used on machine learning and makes use of techniques used in maths such as linear programming, decision trees, neural network. In classification, enterprises try to build tool that can learn how to classify the data items into groups. For instance, a company can define a classification in the application that “given all records of employees who offered to resign from the company, predict the number of individuals who are likely to resign from the company in future.” Under such a scenario, the company can classify the records of employees into two groups that namely “separate” and “retain”. It can use its data mining software to classify the employees into separate groups created earlier. Fuzzy logic resembles human reasoning greatly in handling of imperfect information and can be used as a flexibility tool for soften the boundaries in classification that suits the real problems more efficiently. The present study discusses the meaning of fuzzy logic, its applications and different features. A tool to be build to check data mining algorithms and algorithm behind the model, apply clustering method as a sample in tool to select the training data out of the large data base and reduce complexity and time while computing. K-nearest neighbor method can be used in many applications from general to specific to find the requested data out of huge data. Decision trees – A decision tree is a structure that includes a root node, branches, and leaf nodes. Every one interior node signify a test on an attribute, each branch denotes the result of a test, and each leaf node represents a class label. The topmost node in the tree is the root node. Within the decision tree, we start with a simple question that has multiple answers. Each respond show the way to a further query to help classify or identify the data so that it can be categorized, or so that a prediction can be made based on each answer. Regression analysis is the data mining method of identifying and analyzing the relationship between variables. It is used to identify the likelihood of a specific variable, given the presence of other variables. Outlier detection technique refers to observation of data items in the dataset which do not match an expected pattern or expected behaviour. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or Outlier mining. Sequential Patterns technique helps to find out similar patterns or trends in transaction data for definite period

Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data