Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve the accuracy of existing diagnostic methods for the prediction of Type 2 Diabetes with machine learning algorithms. The proposed algorithm selects the essential features from the Pima Indians Diabetes Dataset with Goldberg's Genetic algorithm in the pre-processing stage and a Multi Objective Evolutionary Fuzzy Classifier is applied on the dataset. This algorithm works on the principle of maximum classifier rate and minimum rules. As a result of feature selection with GA the number of features is reduced to 4 from 8 and the classifier rate is improved to 83.0435 % with NSGA II in training rate of 70% and 30% testing. © 2017 IEEE.

Sasikala R

Department of Computational Intelligence

School of Computer Science and Engineering

Vellore Campus

R. Vaishali

S. Ramasubbareddy

S. Remya

S. Nalluri

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2017 International Conference on Computing Networking and Informatics (ICCNI)

The precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end-to-end learning framework. © 2019 John Wiley &amp; Sons, Ltd.

International Journal of Communication Systems

Estimating rainfall using machine learning strategies based on weather radar data

Neurocomputing

Multi-objective evolutionary feature selection for online sales forecasting

Communication and Computing Systems

Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection

International Journal of Intelligent Systems and Applications

GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis

International Journal of Bio-Science and Bio-Technology

GA_J48graft DT: A Hybrid Intelligent System for Diabetes Disease Diagnosis

Nature Neuroscience

Dimensionality reduction for large-scale neural recordings

Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset

Journal	Data powered by Typeset2017 International Conference on Computing Networking and Informatics (ICCNI)
Publisher	Data powered by TypesetIEEE
Open Access	0