Detection of Coronary Heart Diseases using Data Mining Techniques

In recent times, heart diseases have been prevalent throughout the globe. Millions of people die every year because of misdiagnosis of heart diseases. There is a dire need to develop correct diagnosis of heart related diseases based on data from earlier case scenarios.To aid early and correct diagnosis of these Coronary Heart Diseases, many data mining techniques have been devised using large amount of data available to the hospitals. The various algorithms devised are used to assist physicians in the diagnosis of heart diseases based on various patient parameters such as fasting blood sugar, previous heart events, age, etc. This paper analyses the best of these techniques which include the decision tree algorithm, the Naïve Bayes algorithm and the neural networks algorithm, and enlists the good and the bad in each one of them

Analisis Kinerja Algoritma C4.5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan

- This study describes the application of the algorithm C4.5 on decision support systems to support trainees in PPTIK STIKI Malang in choosing the appropriate type of training. Decision support system based on several criteria derived from the data filled out by participants prior to register as a participant. Further analysis using an algorithm that is used to form a C4.5 decision tree. The decision tree is a method of classification and prediction that represent rules. the rule is then developed using RGFDT (Rule Generation From Decision Tree). Results of testing done by comparing the system with Weka and showed an accuracy of 90%.Keywords—Algorithm C4.5, Decision Support System, RGFD

A Review on Intutive Prediction of Heart Disease Using Data Mining Techniques

International audienceHealthcare evaluates clinical datasets regularly by specialist's learning and action. In the clinical field, computer-supported with prediction system is used in the healthcare department. Data mining approach provides innovation and strategy to replace voluminous information into useful data for achieving a decision. By utilizing information mining systems it needs less investment for the forecast of the sickness with more accuracy and precision. This paper evaluates various classifiers and algorithms are used for the expectation of cardiovascular illness

Artificial intelligence methodologies and their application to diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers?doctors and nurses?in this field

Predicting Arrhythmia Based on Machine Learning Using Improved Harris Hawk Algorithm

Arrhythmia disease is widely recognized as a prominent and lethal ailment on a global scale, resulting in a significant number of fatalities annually. The timely identification of this ailment is crucial for preserving individuals' lives. Machine Learning (ML), a branch of artificial intelligence (AI), has emerged as a highly efficient and cost-effective method for illness detection. The objective of this work is to develop a machine learning (ML) model capable of accurately predicting heart illness by using the Arrhythmia disease dataset, with the purpose of achieving optimal performance. The performance of the model is greatly influenced by the selection of the machine learning method and the features in the dataset for training purposes. In order to mitigate the issue of overfitting caused by the high dimensionality of the features in the Arrhythmia dataset, a reduction of the dataset to a lower dimensional subspace was performed via the improved Harris hawk optimization algorithm (iHHO). The Harris hawk algorithm exhibits a rapid convergence rate and possesses a notable degree of adaptability in its ability to identify optimal characteristics. The performance of the models created with the feature-selected dataset using various machine learning techniques was evaluated and compared. In this work, total seven classifiers like SVM, GB, GNB, RF, LR, DT, and KNN are used to classify the data produced by the iHHO algorithm. The results clearly show the improvement of 3%, 4%, 4%, 9%, 8%, 3%, and 9% with the classifiers KNN, RF, GB, SVM, LR, DT, and GNB respectively

Analisis Kinerja Algoritma C4.5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan

Abstract - This study describes the application of the algorithm C4.5 on decision support systems to support trainees in PPTIK STIKI Malang in choosing the appropriate type of training. Decision support system based on several criteria derived from the data filled out by participants prior to register as a participant. Further analysis using an algorithm that is used to form a C4.5 decision tree. The decision tree is a method of classification and prediction that represent rules. the rule is then developed using RGFDT (Rule Generation From Decision Tree). Results of testing done by comparing the system with Weka and showed an accuracy of 90%. Keywords—Algorithm C4.5, Decision Support System, RGFD

The process and utility of classification and regression tree methodology in nursing research

Aim: This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background: Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design: Discussion paper. Data sources: English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Discussion: Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research: Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion: Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions

Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees

Cardiac rehabilitation is a well-recognised non-pharmacological intervention recommended for the prevention of cardiovascular disease. Numerous studies have produced large amounts of data to examine the above aspects in patient groups. In this paper, datasets collected for over a 10 year period by one Australian hospital are analysed using decision trees to derive prediction rules for the outcome of phase II cardiac rehabilitation. Analysis includes prediction of the outcome of the cardiac rehabilitation program in terms of three groups of cardiovascular risk factors: physiological, psychosocial and performance risk factors. Random forests are used for feature selection to make the models compact and interpretable. Balanced sampling is used to deal with heavily imbalanced class distribution. Experimental results show that the outcome of phase II cardiac rehabilitation in terms of physiological, psychosocial and performance risk factor can be predicted based on initial readings of cholesterol level and hypertension, level achieved in six minute walk test, and Hospital Anxiety and Depression Score (HADS) anxiety score and HADS depression score respectively. This will allow for identifying high risk patient groups and developing personalised cardiac rehabilitation programs for those patients to increase their chances of success and minimize their risk of failure. © 2011, Australian Computer Society, Inc