Visualization of big data is crucial for meaningful interpretations and especially for healthcare. Brief discussions are made for big data, background for healthcare, and recent work in big data analytics for healthcare. This research pertains to different levels of big data: 5,110 vs. 101,766 vs. 320,200 vs. 1 million data values. Data visualizations and predictive analytics are presented of big data for selected diseases of stroke with 5,110 data values, diabetes with 101,766 data values, and two COVID-19 studies: one with 320,200 data values and another with 1 million data values. Data visualizations are generated for these diseases with big data using Tableau. For stroke patients, an investigation was performed to determine how different living environments affect relationship between strokes. The data visualizations for diabetes showed impact of insulin use yielded reduced hospital stays. Data visualizations for COVID-19 provided temporal trends in confirmed cases, mortality, and recovery rates for 2020-2023. Conclusions and future directions of research are presented.

Data mining approaches such as natural language processing play a fundamental role in the healthcare sector and, exclusively, the pharmacology domain. The substantial feedback and experiences shared by the patients on different drugs are employed to perform opinion mining on the reviews, which will help the decision-makers to improve the medications' quality and provide the optimal medical outcomes. Based on that, the drug review data set from the UCI machine learning repository is used. The objective of this study is to conduct a sentiment analysis of the patients' reviews to obtain their satisfaction with different drugs using the random forest (RF) machine learning model. In addition, finding out the best drugs for different conditions based on patients' reviews is done by implementing the long short-term memory. Finally, the authors predict the patients' medical conditions based on their reviews by performing the support vector machine and RF classifiers. The knowledge of the patients' medical condition and satisfaction will lead to a noticeable improvement in the pharmaceutical and medical consequences.

Declining inpatient admissions have serious consequences on hospital financial stability as well as the health of patients. Thus, identifying factors associated with inpatient admissions is crucial to properly manage healthcare services. The major objective of this research is to demonstrate a systematic methodology using regression analysis and no free lunch (NFL) theorem to identify the most significant factors associated with non-COVID-19 ADMs and to identify which of them have deviated from an ideal state of service. This research uses Pennsylvania U.S. hospital data from 2003 to 2018 and identified that bed setup, staffed and supported, average length of stay, occupancy rate, readmission index, and outpatients are significantly associated with ADMs. Further, readmissions and outpatient admissions are found with an unusual association compared to an ideal condition. This paper discusses the steps that U.S. healthcare systems have already implemented and presents improvement recommendations.