Heart Disease Prediction - Classification, Flask, Streamlit, Docker, Feature Selection

Heart disease or Cardiovascular disease is one of the biggest causes of mortality (i.e., causing 1 out of 4 deaths in the US) among the population of the world. Therefore, prediction of Cardiovascular disease is considered one of the important subjects in clinical data analysis. However, several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, etc. lead to cardiac arrest. So, the purpose of this work is to predict if any patient has the chance of having heart disease or not.

April 4, 2017

Analysis of Parkinson Patient - Feature Selection, Classification

The objective is to detect patients with Parkinson’s Disease (PD) from the voice samples. The training data includes voice measurements such as average, maximum, and minimum vocal fundamental frequency, several measures of variation in fundamental frequency, variation in amplitudes, ratio of noise to tonal components, signal fractal scaling exponent and nonlinear measures of fundamental frequency variation. From these measurements, feature-selection (filter and wrapper method) is performed to select essential features for detecting Parkinson.

April 3, 2017

News Classification - LSTM

The objective of this project is to classify news category from articles. The input data consist of 2225 news articles from the BBC news website corresponding to stories in 5 topical areas (e.g., business, entertainment, politics, sport, tech). LSTM has been applied in the classification task to categorize articles. TensorFlow 2.0 has been used to train the model. Word embedding is used in feature generation. TSNE is used to visualize the word vectors in 2d space.