Abstract :

This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

 

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Keywords :

analytics, Decision making, Deep learning, Healthcare, Information, Machine learning, patient data, personalized machine, Prediction

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