In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon’s point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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Implementation of Machine Learning Model to Predict Heart Failure Disease

An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients.

Multi-class pattern classification has many applications including speech recognition, and it is not easy to extend from two-class neural networks (NNs). This paper presents a study about using binary classifiers with NNs together with a perceptual linear prediction (PLP) method for feature extraction to increase the classification rate of voice commands captured using a throat microphone, comparing this method with a single NN. Because there is no other data set with voice commands captured using a throat microphone in the Brazilian Portuguese language in researched literature, we created a data set with isolated voice commands with utterances captured from 150 people (men and women). All the voice samples are captured in Brazilian Portuguese, and they are the digits “0” through “9” and the words “Ok” and “Cancel”. The results show that the throat microphone is robust in noise environment, achieving 95.4% of hit rate in our speech recognition system with multiple NNs using the one-against-all approach, better performance than a simple NN that reach 91.88%. This result is very representative, since both classifiers obtained high hit rates. But, it requires 535% more time for training the multiple NNs compared with simple NN. The best configuration on PLP extraction order is 9 or 10 for voice samples captured by the throat microphone, which was observed that poor stressed vowel and fricative-like words “3” and “7” in Portuguese confuses the classifier.

Binary Neural Networks for Classification of Voice Commands From Throat Microphone

A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.

Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications.

In the field of information extraction and retrieval, binary classification is the process of classifying given document/account on the basis of predefined classes. Sockpuppet detection is based on binary, in which given accounts are detected either sockpuppet or non-sockpuppet. Sockpuppets has become significant issues, in which one can have fake identity for some specific purpose or malicious use. Text categorization is also performed with binary classification. This research synthesizes binary classification in which various approaches for binary classification are discussed.

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Machine Learning: A Review on Binary Classification

Sockpuppet are the fake identity used by some malicious users. In Wikipedia, there are number of sockpuppets present. Two different online accounts, belongs to the same person are called sockpuppet. Sockpuppets has become significant issues, in which one can have fake identity for some specific purpose or malicious use. Sockpuppet detection are based on binary classification in which given accounts are classified either sockpuppet or non-sockpuppet category. This research synthesizes sockpuppets detection in which various approaches are discussed to identify the fake account and legitimate account.

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Roshan Kumari

Papers

Machine Learning: A Review on Binary Classification

A Survey on Sockpuppet Detection over Collaborative Projects