scholarly journals Cardiovascular Disease Prediction and Classification using Modified Teaching Learning Optimization Method

2020 ◽  
Vol 9 (5) ◽  
pp. 808-813
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Social Insurance ◽  
Insurance Industry ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Prognostic Models ◽  
Features Selection ◽  
Teaching Learning ◽  
Clinical Records

Cardiovascular disease (CVD) is possibly the greatest reason for casualty and death rate among the number of inhabitants on the planet. Projection of cardiopathy is viewed as one of the most crucial subjects in the area of clinical records exploration. The measure of information in the social insurance industry is massive. The Data mining process transforms the huge range of unrefined medical service data into meaningful information that can lead to erudite decision and projection. Some recent investigations have applied data exploratory procedures too in CVD estimation. However, only very few studies have revealed the elements that play crucial role in envisioning CVDs. It is imperative to opt for the combination of correct and significant elements that can enhance the functioning of the forecasting prototypes. This study aims to ascertain meaningful elements and data mining procedures that can enrich the correctness of foretelling CVDs. Prognostic models were formulated employing distinctive blend of features selection modified teaching learning optimization techniques, SVM and boosting classification. Here the proposed strategy gives high precision outcomes with existing classification.

scholarly journals Rough set and teaching learning based optimization technique for optimal features selection

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Suresh Satapathy ◽  
Anima Naik ◽  
K. Parvathi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimization Technique ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Features Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

scholarly journals AN EFFECTIVE ARCHETYPE DESIGN OF HEART DISEASE ANTICIPATION USING OPTIMIZATION TECHNIQUES

2019 ◽  
pp. 33-40
Author(s):  
Deepika P ◽  
Saranya S ◽  
Dr.Sasikala S
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Optimization Techniques ◽  
Data Sets ◽  
The Novel ◽  
Human Beings ◽  
Unhealthy Lifestyle ◽  
Initial Finding ◽  
Heart Attacks

Heart Disease is one of the most prevalent diseases that can lead to reduce the life span of human beings now a days. The initial finding of abnormal heart conditions is important to detect heart problems and avoid sudden cardiac death. Heart Disease are also known as Cardiovascular Disease which happen due to unhealthy lifestyle, smoking, alcohol and high intake of fats which may cause hypertension, high blood pressure, diabetes. They are caused by disorders of heart and blood vessels, and includes coronary heart disease (heart attacks). Data mining plays a major role in the construction of an intellectual prediction model for healthcare system to detect Heart Disease (HD) using patient data sets, which support doctors in diminishing mortality rate due to heart attack. In this review, we focus the novel and unique aspects of cardiovascular disease health and the methodologies used to predict the CVD. KEYWORDS: Data mining, Classification, Particle Swarm Optimization, Cardiovascular Disease(CVD).

Emerging Strategies to Big Data Analytics in Healthcare

2021 ◽  
pp. 254-260
Author(s):  
Tanmayee Tushar Parbat ◽  
Rohan Benhal ◽  
Honey Jain ◽  
Dr. Vinayak Musale
Keyword(s):  
Big Data ◽  
Human Services ◽  
Social Insurance ◽  
Insurance Industry ◽  
Big Data Analytics ◽  
Clinical Assessments ◽  
The Social ◽  
Effective Administration ◽  
Clinical Records ◽  
General Wellbeing

Big data is gigantic measures of data that can do some incredible things. It has gotten a subject specifically compelling for as long as two decades in view of a high potential that is covered up in it. Different open and private part ventures create, store, and break down huge information to improve the administrations they give. In the social insurance industry, various hotspots for huge information incorporate emergency clinic records, clinical records of patients, aftereffects of clinical assessments, and gadgets that are a piece of the web of things. Biomedical examination additionally creates a critical bit of enormous information pertinent to open medicinal services. This information requires legitimate administration and examination to determine important data. Something else, looking for an answer by breaking down large information rapidly gets tantamount to finding a needle in the pile. There are different difficulties related with each progression of dealing with huge information which must be outperformed by utilizing very good quality registering answers for huge information investigation. That is the reason, to give significant answers for improving general wellbeing, social insurance suppliers are required to be completely outfitted with proper framework to produce and examine huge information methodically. Effective administration, examination, and understanding of large information can change the game by opening new roads for present day human services. That is exactly why different ventures, including the human services industry, are finding a way to change over this potential into better administrations and budgetary focal points. With a protected mix of biomedical and social insurance information, present day human services associations can upset the clinical treatments and customized medication.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

Data Mining for Cardiovascular Disease Prediction

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Bárbara Martins ◽  
Diana Ferreira ◽  
Cristiana Neto ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Disease Prediction

Harmonic Synthesis Design Methodology for Dynamic Spring Balancing

1996 ◽  
Vol 118 (4) ◽  
pp. 733-740 ◽  
Author(s):  
Eungsoo Shin ◽  
D. A. Streit
Keyword(s):  
Dynamic System ◽  
Initial Conditions ◽  
Phase 1 ◽  
System Optimization ◽  
Optimization Method ◽  
Optimization Techniques ◽  
System Response ◽  
Two Phase ◽  
Walking Machine ◽  
Synthesis Design

A new spring balancing technique, called a two-phase optimization method, is presented. Phase 1 uses harmonic synthesis to provide a system configuration which achieves an approximation to a desired dynamic system response. Phase 2 uses results of harmonic synthesis as initial conditions for dynamic system optimization. Optimization techniques compensate for nonlinearities in machine dynamics. Example applications to robot manipulators and to walking machine legs are presented and discussed.

scholarly journals Review On Application of Data Mining in Life Insurance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 159
Author(s):  
Vaibhav A. Hiwase ◽  
Dr. Avinash J Agrawa
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Life Insurance ◽  
Insurance Industry ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Insurance Company ◽  
Financial Loss ◽  
Life Insurance Company

The growth of life insurance has been mainly depending on the risk of insured people. These risks are unevenly distributed among the people which can be captured from different characteristics and lifestyle. These unknown distribution needs to be analyzed from        historical data and use for underwriting and policy-making in life insurance industry. Traditionally risk is calculated from selected     features known as risk factors but today it becomes important to know these risk factors in high dimensional feature space. Clustering in high dimensional feature is a challenging task mainly because of the curse of dimensionality and noisy features. Hence the use of data mining and machine learning techniques should experiment to see some interesting pattern and behaviour. This will help life insurance company to protect from financial loss to the insured person and company as well. This paper focuses on analyzing hidden correlation among features and use it for risk calculation of an individual customer.  

scholarly journals Performance Analysis of Rule Based Algorithms Applied to a Cardiovascular Dataset

2013 ◽  
Vol 12 (2) ◽  
pp. 3277-3285
Author(s):  
Dev Mukherji ◽  
Nikita Padalia
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Alternative Methods ◽  
Support Vector ◽  
Rule Based ◽  
Prevention And Treatment ◽  
Predictive Methods ◽  
Single Data

Cardiovascular disease is one of the dominant concerns of society, affecting millions of people each year. Early and accurate diagnosis of risk of heart disease is one of major areas of medical research, aimed to aid in its prevention and treatment. Most of the approaches used to predict the occurrence of heart disease use single data mining techniques. However, performances of predictive methods have recently increased upon research into hybrid and alternative methods. This paper analyses the performance of logistic regression, support vector machine, and decision trees along with rule-based hybrids of the three in an attempt to create a more accurate predictive model.

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