This paper argued that patient data analysis is vital to healthcare machine learning, delivering insights that help enhance diagnosis, treatment, and patient care. Healthcare systems use electronic health records, medical imaging data, and real-time physiological measurements from wearable devices. It recognises the complexity and diversity of various data sources and uses advanced machine-learning to find patterns and information. Machine learning can also use patient-specific data to make personalised therapy recommendations, improving outcomes. TF-IDF and Blowfish were employed. It is the number of times a term appears in a document divided by the total terms. Frequent terms in a paper may be more important. It suggests better diagnostics, personalised therapy, illness prevention, and resource allocation. Machine learning and patient data analysis help healthcare providers customise treatment plans, anticipate illness development, and deliver more effective and focused interventions. It helps distinguish significant document terms from common words with little meaning. TF-IDF uses local term frequency and global corpus statistics to capture term specificity and relevance in document collections. For missing values, outliers, and inconsistent formats, raw patient data needs preparation. Blowfish has been extensively analysed since its conception and found to have no obvious design flaws. Blowfish is flexible and adaptable to diverse security needs because it provides key lengths from 32 to 448 bits. The encryption is more secure with longer keys. Data cleansing, normalisation, and standardisation are preprocessing steps. Data quality checks find and fix data anomalies.