International Journal of Computer and Applications

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches.

Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Data preprocessing for anomaly based network intrusion detection : a review

Review: Review of multicast routing mechanisms in mobile ad hoc networks

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

The Healthcare trade usually clinical diagnosis is ended typically by doctor’s knowledge and practice. Computer Aided Decision Support System plays a major task in medical field. Data mining provides the methodology and technology to alter these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. Among the increasing research on heart disease predicting system, it has happened to significant to categories the research outcomes and gives readers with an outline of the existing heart disease prediction techniques in each category. Data mining tools can answer trade questions that conventionally in use much time overriding to decide. In this paper we study different papers in which one or more algorithms of data mining used for the prediction of heart disease. As of the study it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system. The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

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Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review

Software Security and Intrusion Detection need to be dealt at three levels Network, Host level and Application level. In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.

https://lib.jucs.org/article/23130/download/pdf/

An Approach for Intrusion Detection Using Novel Gaussian Based Kernel Function

Data mining technique in the history of medical data found with enormous investigations resulted that the prediction of heart disease is very important in medical science. The data from medical history has been found as heterogeneous data and it seems that the various forms of data should be interpreted to predict the heart disease of a patient. Various techniques in Data Mining have been applied to predict the patients of heart disease. But, the uncertainty in data was not removed with the techniques available in data mining. To remove uncertainty, it has been made an attempt by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum distance K-NN classifier was incorporated to classify the data among various groups. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.

Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach

Precision agriculture is the technology driven approach for optimizing farm management in terms of inputs and outputs besides preserving resources Towards this end many techniques came into existence Data mining techniques are can be used towards precision agriculture Numerous efforts have been made to exploit remote sensing data to build various indices for assessing productivity of crops They include Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Normalized Difference Vegetation Index (NDVI) Crop yield prediction can help agriculture related departments and organizations to make strategic decisions In this paper a novel framework named eXtensible Crop Yield Prediction Framework (XCYPF) is proposed that is flexible and extensible It has provision for selection of crop, dependent and independent variables, datasets for crop yield prediction towards precision agriculture The available indices are used along with rainfall data and surface temperature for crop yield prediction for rice and sugarcane crops

XCYPF: A flexible and extensible framework for agricultural Crop Yield Prediction

Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart patient Various techniques in Data Mining have been applied to predict the heart disease patients But, the uncertainty in data was not removed with the techniques available in data mining and implemented by various authors To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data A membership function was designed and incorporated with the measured value to remove uncertainty and fuzzified data was used to predict the heart disease patients Further, an attempt was made to classify the patients based on the attributes collected from medical field Minimum Euclidean distance Fuzzy K-NN classifier was designed to classify the training and testing data belonging to different classes It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques

Gugulothu Narsimha

Papers

Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review

An Approach for Intrusion Detection Using Novel Gaussian Based Kernel Function

Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach

XCYPF: A flexible and extensible framework for agricultural Crop Yield Prediction

Diagnosis of heart disease patients using fuzzy classification technique