Machine learning is one of the fast growing aspect in current world. Machine learning (ML) and Artificial Neural Network (ANN) are helpful in detection and diagnosis of various heart diseases. Naïve Bayes Classification is a vital approach of classification in machine learning. The heart disease consists of set of range disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using Naïve Bayes classification and support vector machines. The classification accuracy of the patients suffering from heart disease is predicted using Naïve Bayes classification and support vector machines. Implementation is done using R language.

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This work presents a frequency domain ultrawideband receiver architecture spanning from 500 MHz to 10.5 GHz. Proposed architecture uses Transform Domain (TD) sampling method which relaxes the ADC performance bottleneck for digitizing 500 MHz baseband signal. To achieve wideband operation a RF front end consisting of wideband LNA, VGA, baseband mixer and integrator is designed using UMC 130nm technology. Due to wide frequency operation this architecture is suitable for software defined radio applications.

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