{'fraction_non_alphanumeric': 0.019455252918287938, 'fraction_numerical': 0.0, 'mean_word_length': 4.716666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards a decision support system by the study of cell malfunctions for breast cancer

Breast cancer is one of the leading cause of death for women today and it is the most common cancer in developed countries. The cause and degree of the breast cancer are very much associated with the malfunctions of its tissues and cells. It is very hard and rigorous task for the doctors to observe the clinical records for many affected patients and regulate the therapy manually. Therefore, it is very much necessary to properly process the bulk amount of clinical records (containing cell details) automatically and come with the best possible treatment for the affected patients. In this work we have proposed a decision support system with the help of two data mining techniques; namely, decision tree learning and association rules mining. Clinical data have been studied, pre-processed and analyzed with the help of a data mining tool (e.g., WEKA). Finally, as an outcome we come with the decision support tool for practical purpose.