There is great diversity in the field of medical science due to computational power and technical innovation, especially in identifying human heart disease. Today it is one of the deadliest human heart diseases in the world and have very serious effects on human life. Accurate and timely identification of heart disease in humans can be very helpful in prevent heart failure in its early stages and will improve patient survival. Manual method for determining the heart disease is biased and can vary between researchers. In this regard, efficient and reliable machine learning algorithms resources for detecting and classifying people with heart disease and those who are healthy. According to suggestion in our study, we identified and predicted heart disease in humans using a variety of machine learning algorithms and using heart disease dataset to evaluate its performance using various measures, such as sensitivity, specificity, F-measure, and classifier accuracy. For this purpose, we used nine machine learning classifiers for the final dataset before and after hyper parameter tuning of machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. In addition, we verify their accuracy on a standard heart disease dataset by performing several standardized, pre-processing procedures of the data set and hyper parameter tuning. In addition, to train and validate machine learning algorithms, we implemented standard K-fold cross-validation technique. Finally, the experimental results show that the accuracy of the predictive classifiers with improved hyper parameter tuning and achieved remarkable results with data normalization and hyper parameter tuning of machine learning classification.

In the last two decades, Information and Communication Technologies (ICTs) have played a pivotal role in empowering rural populations in India by making knowledge more accessible. Digital Green is one such ICT that employs a participatory approach with smallholder farmers to produce instructional agricultural videos that encompass content specific to them. With the help of human mediators, they disseminate these videos to farmers using projectors to improve the adoption of agricultural practices. Digital Green's web-based data tracker (CoCo) stores the attendance and adoption logs of millions of farmers, the videos screened to them and their demographic information. In our work, we leverage this data for a period of ten years between 2010-2020 across five states in India where Digital Green is most active and use it to conduct a holistic evaluation of the ICT. First, we find disparities in the adoption rates of farmers, following which we use statistical tests to identify the different factors that lead to these disparities as well as gender-based inequalities. We find that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller

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