Data Mining - Concepts and Techniques.

Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naive Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

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Comparing different supervised machine learning algorithms for disease prediction

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still „information rich‟ but „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

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Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.

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A survey on Data Mining approaches for Healthcare

This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms - Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.

Association rule mining to detect factors which contribute to heart disease in males and females

The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.

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Intelligent heart disease prediction system using data mining techniques

Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification.

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A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA

The Big data and cloud integration is a challenging Task. To enhance the data security issues, ABE can be deployed. In proposed model, a improved concept has been implemented and the integration of cloud and Big data is achieved. Security is the major threat for cloud computing applications. Every user has to feed user name, password, and primary key for Data access into the cloud data center. Data owner generates a new key to the users for accessing the data. Policy updating is also implemented in the proposed system, that is the accountability for the data access has also been implemented. In case of the change of policy, the altered data stored in the cloud is not affected. In addition to that, admin generates policy key based on the user’s profile. If any user tries to misbehave, an immediate alert is sent to the data owner. Data owner can change the policy key and access policy in the run time. Our system should be able to update its policy automatically.

A Novel Approach to View and Modify Data in Cloud Environment Using Attribute-Based Encryption

Images captured using smartphones and video cameras are recorded and can be used anywhere and at any time. While taking a quick shot or while capturing the moving objects, it may lead to the motion blurred images. In order to recover the sharp images from motion blurred images, blind motion deblurring can be used. Motion deblurring can be done by knowing both edge and non-edge of motion blurred images. Edge and non-edge are the two methods used in total variation and alternate direction method for deblurring of digital images. Step edges can be predicted and detected by using edge-specific method. In non-edge method, it explores various image statistics, such as the prior distributions and it is sensitive to statistical variation over different images. Both methods are used in large dataset images, but it fails extremely in simple images. To overcome this problem, total variation (TV) based regularization method is used which is followed by an iteratively reweighted algorithm based on alternating direction method. To get higher results, LSED prediction—based technique is employed, which first of all restores sharp edges and then uses them to estimate initial kernel that traps the optimization of local minimum corresponding to sharp images.

Sellappan Palaniappan

Papers

Intelligent heart disease prediction system using data mining techniques

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA

A Novel Approach to View and Modify Data in Cloud Environment Using Attribute-Based Encryption

Total Variation and Alternate Direction Method for Deblurring of Digital Images