J. Vijayashree

Bio: J. Vijayashree is an academic researcher from VIT University. The author has contributed to research in topics: Deep learning & Neocognitron. The author has an hindex of 2, co-authored 2 publications receiving 34 citations.

A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier

Abstract: Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.

Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network

Abstract: According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results.

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Abstract: Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS

Abstract: The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT) The IoMT has an important role to play in reducing the mortality rate by the early detection of disease The prediction of heart disease is a key issue in the analysis of clinical dataset The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima The learning parameters are optimized utilizing MSSO to provide better results for ANFIS The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc The heart condition is identified by classifying the received sensor data using MSSO-ANFIS A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches The proposed MSSO-ANFIS prediction model obtains an accuracy of 9945 with a precision of 9654, which is higher than the other approaches

A Secure Framework for Authentication and Encryption Using Improved ECC for IoT-Based Medical Sensor Data

Abstract: Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors. This has attracted attention for a secure communication framework to access e-health data on mobile devices. The wearable sensor device is attached to the patient's body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc. In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server. The patient's biometrics information has been added as a parameter in addition to the user name and password. The authentication scheme is coined with the SHA-512 algorithm that ensures integrity. To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC). Whereas in improved ECC, an additional key (secret key) is generated to enhance the system's security. In this way, the intricacy of the two phases is augmented. The computational cost of the scheme in the proposed framework is 4H + Ec + Dc which is less than the existing schemes. The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm. The obtained encryption and decryption time are 1.032 μs and 1.004μs respectively. The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest-Shamir-Adleman (RSA)and ECC algorithms.

MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

Abstract: Cardiovascular disease tops the list among all major causes of deaths worldwide. Though, prognostication and in-time diagnosis can help in reducing the mortality rate as well as increases the survival rate of patients. Unavailability or scarcity of radiologists and doctors in different countries due to several reasons is a significant factor for hindrance in early diagnosis. Among various efforts of developing the decision support systems, computational intelligence is an emerging trend in the field of medical imaging to detect, prognosticate and diagnose the disease. It helps radiologists and doctors to get relief from being over-burdened and minimizes the induced delays for in-time diagnosis of patients. In this work, a machine intelligence framework for heart disease diagnosis MIFH has been proposed. MIFH utilizes the factor analysis of mixed data (FAMD) to extract as well as derive features from the UCI heart disease Cleveland dataset and train the machine learning predictive models. The framework MIFH is validated using the holdout validation scheme. Experimentation results show that MIFH performed well over several baseline methods of recent times in terms of accuracy and comparable in terms of sensitivity and specificity. MIFH returns best possible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose heart patients.