This heart disease dataset is curated by combining 3 popular heart disease datasets. The first dataset (Collected from Kaggle)  contains 70000 records with 11 independent features which makes it the largest heart disease dataset available so far for research purposes. These data were collected at the moment of medical examination and information given by the patient. Second and third datasets contain 303 and 293 intstances respectively with 13 common features. The three datasets used for its curation are:

1. Cardio Data (Kaggle Dataset)

This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:

Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.

Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.