Online ISSN: 2515-8260

Keywords : Linear Regression

Performance Study of ML Models and Neural Networks for Detection of Parkinson Disease using Dysarthria Symptoms

Harisudha Kuresan; Dhanalakshmi Samiappan; Arathy Jeevan; Sukirti Gupta

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 767-779

Parkinson Disease (PD) is brain disorder that affects the central nervous system
that results in damage of nerve cells causing dopamine to drop. PD has a severe effect on
vocal features termed as Dysarthria symptoms including varied pitch, extended pauses,
monotonous and speaking slowly or with a slur. In this paper, a dataset containing various
vocal features are taken as input to analyze the performance of various Machine Learning
algorithms including Naive Bayes, Random Forest Classifier, Support Vector Machines
(SVM), Linear Regression, K Nearest Neighbor (KNN) and Neural Networks such as ANN
and LSTM. The best classification accuracy was obtained by ANN around 90.00%.

Movie Prior Release Box Office Prediction A Machine Learning Based Approach

V. Gangadhara Reddy; K. Radheer Reddy; A. Krishnamoorthy; R. Kannadasan; P. Boominathan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5016-528

The “Movie Box Office Prediction” includes different factors that influence the movie revenue at the Box Office. Some of the factors include Budget, Genres, Spoken languages, Cast, Crew. In this paper, various plots are made in order to understand and observe the relations between the variables and the amount of effect of factors on the Revenue. Linear Regression, Random Forest and XGBoost are the models used for Training and Testing the Data.