{'fraction_non_alphanumeric': 0.020935960591133004, 'fraction_numerical': 0.003694581280788177, 'mean_word_length': 5.3023255813953485, '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}}
Machine Learning for Ischaemic Heart Disease Diagnostic Analysis

Ischaemic Heart Disease (IHD) is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly and potentially invasive procedures. To reduce the cost and risk to patients, we present a functioning machine learning (ML) pipeline and developed ML predictive models for IHD diagnosis using a limited number of risk factors in a clinical dataset. We fine-tuned and compared a number of learning algorithms and feature selection methods. The effectiveness of proposed methods was evaluated with quantitative measures and clinician input. The best accuracy of 92.9% was achieved, demonstrating a promising tool for screening IHD patients. Practical issues and data bias when applying ML on medical data are discussed.