Hybrid Approach for Heart Disease Detection Using Clustering and ANN

Data mining is a process of extracting data from data set and transforming it into understandable structure for further use. Data mining techniques have been applied magnificently in many fields including business, science and bio informatics, and on different types of data like textual, visual, spatial, and real-time and sensor data. Heart disease prediction is treated as most difficult task in the field of medical sciences. Heart disease detection using data mining can answer complicated queries for diagnosing heart disease and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing effective treatments, it also helps to reduce treatment costs. The aim of this study is to develop an artificial neural networks-based diagnostic model for heart disease using a complex of traditional and genetic factors of this disease

Diagnosis and Prognosis of Breast Cancer Using Multi Classification Algorithm

Data mining is the process of analysing data from different views points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression,Segmentation algorithms, Association algorithms, Sequence analysis algorithms, etc.,. The classification algorithm can be usedto bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. A genetic algorithm (GA) is aheuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer data set, from the given data set using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for breast cancer and primary tumor. Our method of creating new algorithm GA+ID3 easily identifies breast cancer data set from the given data set. The multi classification algorithm diagnosis and prognosis of breast cancer data set is identified by this paper

Enhancing Accuracy of Disease Prediction of KNN and Euclidean Distance using Hybrid Approach

Health monitoring is critical issue associated with now day lifestyle. Lack of time is causing serious issues corresponding to health. Proposed literature focus on this key aspect and provide mechanism to generate accurate predictions corresponding to parameters fetched from dataset. Hybrid approach of K Nearest neighbour and Euclidean distance is used for enhancement in health prediction. For demonstration dataset derived from UCI is utilized. Simulation results suggest considerable improvement over KNN and Euclidean distance mechanism during prediction

Application of Machine Learning for Heart Disease Classification Using Naive Bayes

The Naive Bayes classifier uses an approximation of a Bayes theorem by combining previous knowledge with new ones. The purpose of this research is to develop machine learning using Naive Bayes classification techniques and as a decision system in producing fast and accurate classification accuracy in diagnosing cardiovascular diseases such as heart disease. Cardiovascular disease is the leading cause of death, 32% of all global deaths, of which 85% are caused by stroke and heart disease. Based on the results of the analysis, it was found that the accuracy of classification accuracy in the training data on patient data was classified as having and not having heart disease, respectively 83,21% and 83,1%. In data testing, the percentage of patient data classified as having and not having heart disease was 83,78% and 87,50%, respectively. Based on the AUC values ​​in the training data and testing data, they are 83,15% and 85,24%, respectively. So, from these results, it can be concluded that the Naive Bayes method is good for classifying heart disease patient data

PERANCANGAN DAN IMPLEMENTASI ALAT PENGUKUR KADAR NATRIUM DALAM CAIRAN

Elektrolit adalah senyawa yang sangat penting untuk mendukung proses metabolisme dalam tubuh. Alat untuk melakukan pengukuran kadar elektrolit dalam darah biasa disebut Electrolyte Analyzer. Alat yang tersedia saat ini memiliki harga yang relatif mahal dikarenakan harus di import dari luar negeri. Oleh karena itu, penulis mencoba membuat perangkat pendeteksi elektrolit yang sederhana. Komponen terpenting dari alat ini adalah Ion Selective Electrode untuk mengukur tegangan dalam cairan elektrolit. Selain itu, terdapat komponen pendukung seperti multimeter. Setelah mendapatkan data, data akan dibagi menjadi 2 yaitu data uji dan data latih untuk mengelompokan kadar elektrolit. Pengelompokan menggunakan metode klasifikasi k-Nearest Neighbour (k-NN) ke dalam kondisi normal, hipoatremia, dan hiperatremia. Hasil akhir dari penelitian ini adalah sebuah alat yang digunakan untuk melakukan pengukuran kadar elektrolit dalam cairan dan dikelompokan dengan Matlab. Data diambil dari cairan sampel dengan konsentrasi 110, 115, 120, 125, 130, 135, 140, 145, 150, dan 154 mmol/L. Pengujian yang dilakukan adalah penentuan nilai kadar elektrolit dan pengujian waktu kalibrasi yang memperoleh tingkat akurasi 99,7% dengan skema melalukan kalibrasi setiap satu kali pembacaan cairan sampel. Sedangkan untuk pengelompokan, nilai akurasi tertinggi adalah 75% dengan menggunakan metode k-NN dengan pengukuran jarak Euclidean, City-Block, Chebychev, dan Minkowski dengan nilai k=1 dan k=3

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by removing unneeded or irrelevant features. Various algorithms have been used in healthcare to solve such problems involving complex medical data. This paper demonstrates how Genetic Algorithms offer a natural way to solve feature selection amongst data sets, where the fittest individual choice of variables is preserved over different generations. In this paper, a Genetic Algorithm is introduced as a feature selection method and shown to be effective in aiding understanding of such data

Classification of Poverty Levels Using k-Nearest Neighbor and Learning Vector Quantization Methods

Poverty is the inability of individuals to fulfill the minimum basic needs for a decent life. The problem of poverty is one of the fundamental problems that become the central attention of the local government. One of the government efforts to overcome poverty is using the alleviation programs. Government often faces some difficulties to sort out of the poverty levels in the society. Therefore it is necessary to conduct a study that helps the government to identify the poverty level so that the aid did not miss the targets. In order to tackle this problem, this paper leverages two classification methods: k-nearest neighbor (k-NN) and learning vector quantization (LVQ). The purpose of this study is to compare the accuracy of the value of both methods for classifying poverty levels. The data attributes that are used to characterize poverty among others include: aspects of housing, health, education, economics and income. From the testing results using both methods, the accuracy of k-NN is 93.52%, and the accuracy of LVQ is 75.93%. It can be concluded that the classification of poverty levels using k-NN method gives better performance than using LVQ method

Prediction of Heart Disease using Machine Learning Algorithms: A Survey

According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field