Heart Disease Prediction Model Using Naïve Bayes Algorithm and Machine Learning Techniques

Gene Prediction Using Machine Learning Techniques

The basic purpose of the research work aims at predicting the genes of interest in molecular sequence databases using machine learning techniques like neural networks, decision trees, data mining, hidden markov models etc The primary focus of the research will be on proposing new or improving already existing ab initio and homology based methods for gene prediction. The proposed methods will be...

Email Worm Detection Using Naïve Bayes and Support Vector Machine

Stock Price Prediction using Machine Learning and Swarm Intelligence

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

Link prediction in complex networks: a local naïve Bayes model

Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions...

Heart Disease Prediction Using Data Mining Techniques

There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using...