Enormous data growth in multiple domains has posed a great challenge for data processing and analysis techniques. In particular, the traditional record maintenance strategy has been replaced in the healthcare system. It is vital to develop a model that is able to handle the huge amount of e-healthcare data efficiently. In this paper, the challenging tasks of selecting critical features from the enormous set of available features and diagnosing heart disease are carried out. Feature selection is one of the most widely used pre-processing steps in classification problems. A modified differential evolution (DE) algorithm is used to perform feature selection for cardiovascular disease and optimization of selected features. Of the 10 available strategies for the traditional DE algorithm, the seventh strategy, which is represented by DE/rand/2/exp, is considered for comparative study. The performance analysis of the developed modified DE strategy is given in this paper. With the selected critical features, prediction of heart disease is carried out using fuzzy AHP and a feed-forward neural network. Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated in this paper. The accuracy of the proposed hybrid model is 83%, which is higher than that of some other existing models. In addition, the prediction time of the proposed hybrid model is also evaluated and has shown promising results.

Vivekanandan S

Department of Instrumentation

School of Electrical Engineering

Vellore Campus

Sriman Narayana Iyengar N.C.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Computers in Biology and Medicine

In the rural side, there is the absence of centers for cardiovascular ailment. Due to this, around 12 million people passing worldwide reported by WHO. The principal purpose of coronary illness is a propensity for smoking. ML classifiers are applied to predict the risk of cardiovascular disease. However, the ML model has some inherent problems like it’s serene to feature selection, splitting attribute, and imbalanced datasets prediction. Most of the mass datasets have multi-class labels, but their combinations are in different proportions. In this paper, we experiment with our system with Cleveland’s heart samples from the UCI repository. Our cluster-based DT learning (CDTL) mainly includes five key stages. At first, the original set has partitioned through target label distribution. From the high distribution samples, the other possible class combination has made. For each class-set combination, the significant features have identified through entropy. With the significant critical features, an entropy-based partition has made. At last, on these entropy clusters, RF performance is made through significant and all features in the prediction of heart disease. From our CDTL approach, the RF classifier achieves 89.30% improved prediction accuracy from 76.70% accuracy (without CDTL). Hence, the error rate of RF with CDTL has significantly reduced from 23.30 to 9.70%. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Evolutionary Intelligence

Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction

In the modern era, the vehicles are made for the comfort of the humans and the automation of the vehicle focusses on the safety and comfort of the humans by giving them relaxing time. In the field of automobile industries, technological progress are taking place to a greater extent and to make a vehicle automated various aspects have to be taken into consideration. In this paper, we have focussed on the application in which the vehicle system knows the shortest route between the two destinations and follow this route based on its intuition from the surrounding to take further direction or not. The idea described in this paper has somewhat been taken from Google Car, i.e. Dynamic routing. This can be done using the neural network in which a method of intuition is applied and an artificial brain is to be integrated into the system that decides the shortest path through which one can reach its destination and follow that path based on its data that it collected from the surroundings. Since taking any intelligent decisions in the traffic is also an issue for the automated vehicle system. This aspect has been taken into consideration in this paper. © 2020, Springer Nature Singapore Pte Ltd.

Frontiers in Intelligent Computing: Theory and Applications Advances in Intelligent Systems and Computing

Intuition-Based Autonomous Vehicle System

Cardiovascular disease (CVD) refers to a state that indicates narrowed or blocked blood vessels, and it can lead to cardiac arrest, chest pain (angina) or stroke. CVD is a leading cause of silent massive heart attacks and is a major threat to life. The mere prediction of the presence or absence of CVD alone is inefficient in current scenarios. Rather, a major need has arisen for the prediction of CVD, the acquisition of knowledge about CVD and the assessment of the likelihood that an individual will experience cardiac arrest. The objective of establishing an individual CVD risk assessment has been attained in this paper using a hybrid model. The CVD of an individual is due to various controllable and uncontrollable factors. The computation and analysis of all these factors are difficult and time consuming. Only a few attributes are identified to be the most critical. This optimization of the critical features is performed using a modified Differential Evolution (DE) algorithm. The identified critical factors are sufficient to predict the presence/absence of CVD. In this paper, these identified critical features of individuals are considered using Cox regression analysis that evaluates the prevalence rates of the critical attributes. These individual prevalence rates together predict the cumulative prevalence ratios of the respective individuals. This cumulative prevalence ratio of an individual, along with the class attribute, is processed using the 2-means clustering technique to determine the risk of a particular individual developing CVD. The evaluation of the risk assessment model is carried out in this paper by calculating the prediction accuracy of the Cox regression analysis and the Davies–Bouldin (DB) index for 2-means clustering. The Cox regression analysis results in a 91% CVD prediction accuracy using the critical attributes and is comparatively higher than that of other models. The DB index of 2-means clustering with specific initial means for clusters of individuals with CVD is 0.282 and that for clusters of individuals without CVD is 0.2836, which are comparatively lower than those of the traditional k-means clustering algorithm. © 2019

A Hybrid Risk Assessment Model for Cardiovascular Disease Using Cox Regression Analysis and a 2-means clustering algorithm

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).

Fulltext

IEEE Access

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Background: The rapid growth of data in the healthcare domain is a great challenge for traditional data management system for handling and processing such a huge volume of data. The massive growth of data is inevitable and there arises a quest for identifying an effective storage mechanism which can handle vast dynamic data. The advances in technology have paved way for a solution by means of cloud storage. In the current scenario, Cardio Vascular Disease is the major cause of human mortality across the world. This analysis is the hardcore need in today’s medical research for prediction of Cardio Vascular Disease. Methods: Hence, in this paper, the Heart Disease dataset is taken for analysis. Various experiments have been carried out with the dataset to compare the performance of classification algorithms and Support Vector Machine is found to outperform other algorithms. Conclusion: Due to its limitation in handling big data, Parallel Support Vector Machine is adapted for big data analysis. © 2018 Bentham Science Publishers.

Journal	Data powered by TypesetComputers in Biology and Medicine
Publisher	Data powered by TypesetElsevier BV
ISSN	0010-4825
Open Access	0