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Abstract

Predictive analytics plays a vital role in transforming healthcare by improving patient care, reducing costs, and optimizing resource allocation. As technology continues to advance and healthcare systems become more data- driven, the benefits of predictive analytics are likely to expand, contributing to better healthcare outcomes for individuals and populations alike. Predictive analytics is transforming the healthcare landscape by enhancing early disease detection and prevention. By harnessing the power of data and artificial intelligence, healthcare providers can offer more personalized, effective, and cost-efficient care. While challenges exist, the potential to save lives and Improve overall healthcare outcomes. Makes predictive analytics an Indispensable tool in the fight against diseases. As technology continues to advance, the impact of predictive analytics in early disease detection and prevention will only become more pronounced, reshaping the future of healthcare In the dynamic landscape of healthcare, where innovation is the compass guiding us forward. It is al journey marked by innovation, data-driven insights, and the relentless pursuit of proactive healthcare practices that hold the potential to usher in a new era of disease- prevention and early intervention. Through this review, we aim to illuminate the path ahead, recognizing both the remarkable accomplishments and the challenges that lie on the horizon as we hamess the power of predictive analytics to transform the future of healthcare. These models learn patterns and relationships in the data. In the realm of medical imaging, Al-powered tools are augmenting the capabilities of healthcare professionals. Predictive analytics in healthcare refers to the analysis of current and historical healthcare data that allows healthcare professionals to find opportunities to make more effective and more efficient operational and clinical decisions, predict trends, and even manage the spread of diseases. The proposed system offers a broad disease prognosis based on patient’s symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person’s living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree.

Keywords

Predictive Analytic

Reference

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Hariom Rajput
Corresponding author

Yeshwant Niwas Rd, Lantern Square, Yeshwant Colony, Indore, Madhya Pradesh

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Sanoop Kumar Tiwari
Co-author

Yeshwant Niwas Rd, Lantern Square, Yeshwant Colony, Indore, Madhya Pradesh

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Co-author

Hariom Rajput*, Sanoop Kumar Tiwari, Detection Of Diseases And Predictive Analytic, Int. J. in Pharm. Sci., 2023, Vol 1, Issue 11, 180-191. https://doi.org/10.5281/zenodo.10084620

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