V. Krishnaiah

Bio: V. Krishnaiah is an academic researcher from Jawaharlal Nehru Technological University, Hyderabad. The author has contributed to research in topics: Decision support system & Fuzzy classification. The author has an hindex of 2, co-authored 2 publications receiving 58 citations.

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

Abstract: 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.

Diagnosis of heart disease patients using fuzzy classification technique

Abstract: 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

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Abstract: 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).

Heart Disease Prediction using Machine Learning Techniques

Abstract: Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

Concepts and techniques

Abstract: 1 The Modern Microscope Today.- 2 The Quest for Ultra-High Resolution.- 3 Z-Contrast Imaging in the Scanning Transmission Electron Microscope.- 4 Inelastic Scattering in Electron Microscopy-Effects, Spectrometry and Imaging.- 5 Quantitative Analysis of High-Resolution Atomic Images.- 6 Electron Crystallography-Structure determination by combining HREM, Crystallographic image processing and electron diffraction.- 7 Electron Amorphography.- 8 Weak-Beam Electron Microscopy.- 9 Point Group and Space Group Identification by Convergent Beam Electron Diffraction.- 10 Advanced Techniques in TEM Specimen Preparation.

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Abstract: Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.