Munshi Imran Hossain

Munshi Imran Hossain is a Software Affiliate at Cytel and currently works as a data scientist for strategic consulting assignments. He has 5 years of experience working in the areas of software development and data science. He holds an M. Tech in Biomedical Engineering, from IIT Bombay. His interests include processing and analysis of biomedical data. Outside of work, he enjoys reading.

Recent Posts

Addressing the Problem of Feature Selection Using Genetic Algorithms

Posted by Munshi Imran Hossain

Jan 15, 2018 8:12:00 AM

The problem of feature selection

The explosion in the availability of big data has made complex prediction models a conspicuous reality of our times. Whether in banking, financial services and insurance, telecoms, manufacturing or healthcare, predictive models are increasingly used to derive inference from data.

Most of these models use a set of input variables, called features, to predict the output on a variable of interest. For example, the concentration of characteristic biomarkers in the blood can be used to predict the presence, absence or progress of certain diseases.

The available data can provide a large number of features, but generally, it’s preferable to use a small number of really relevant features in a model. This is because a model with more features has a greater complexity which leads to greater demand on computational resources and time to train the model. Therefore it is desirable to restrict the number of features in a predictive model. Choosing the subset of features that will result in a model with optimum performance is the problem of feature selection. This is essentially a problem of plenty.

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Topics: big data, data science, genetic algorithm


Removing 'Noise' from Biomedical Signals

Posted by Munshi Imran Hossain

Sep 7, 2017 7:00:24 AM

By Munshi Imran Hossain, Software Affiliate at Cytel

Biomedical signals are electrical signals collected from the body. Some of the most common ones are the electrocardiogram (ECG) and the electroencephalogram (EEG). These signals are of great value because they can be used for diagnostic purposes. Importantly, most of them can be collected using non-invasive methods. These attributes, together with the tremendous recent advances in electronic and digital processing technology, have made biomedical signal data an important source of data used in medical diagnostics.

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Topics: signal processing, data science, biomedical signals, Fourier


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