Description:FIELD OF THE INVENTION
[1] The goal of the current invention is to significantly progress and improve a variety of businesses, especially the technology sector, by applying artificial intelligence (AI) to the field of predictive business analytics. It centers on utilizing AI's capabilities to transform how companies in a variety of industries evaluate and use data for strategic decision-making.
BACKGROUND AND PRIOR ART OF THE INVENTION
[2] The idea of predictive analytics is not as new as the broad application of artificial intelligence. For a long time, traditional statistical methods and modelling techniques have been used to forecast future events based on past data. Predictive analytics' accuracy and applicability have, however, greatly increased since the development of AI. As big datasets become more widely available and machine learning develops,
[3] Yet, artificial intelligence has developed into a potent instrument for forecasting. Business analytics are being approached differently thanks to AI systems' capacity to learn from data, spot trends, and forecast outcomes. One important breakthrough is the incorporation of AI into corporate intelligence tools. By combining historical data analysis with predictive analytics, this integration helps organizations make more proactive decisions by allowing them to forecast future trends and results.
[4] Predictive analytics has made use of a variety of machine learning methods, including neural networks, decision trees, and regression analysis. Several methods for training and fine-tuning these models for particular commercial use cases are included in the previous art in this field. Predictive analytics has been applied in a variety of industries, with an emphasis on problems unique to each sector. Predictive analytics, for instance, can be used to forecast patient outcomes in the healthcare industry and market trends in the finance industry.
[5] Real-time analytics breakthroughs that enable firms to make quick judgements and forecasts are likewise included in the category of prior art. This entails creating algorithms that can handle and analyses data in real-time, which is essential in dynamic corporate settings. Prior art takes ethical and privacy concerns into account when artificial intelligence (AI) is used in analytics to a greater extent. Innovation has focused on finding a way to balance the advantages of predictive analytics with appropriate data usage and privacy protection.
[6] Developments in predictive analytics have been impacted by the move to cloud computing. Cloud-based, scalable predictive analytics tools that are easily accessible to companies of all sizes are examples of prior art. These developments have enormous potential to provide practical, environmentally sustainable solutions for health and environmental problems.
[7] United States Patent No. US7974714B2 reveals a technique for creating an intelligent electronic appliance, which ideally has an intelligent processor, a data input and/or output port, and an interface. One desired embodiment includes a set-top box with an adaptive user interface, media processing based on content or media metadata, and telecoms integration for dealing with broadband media streams. By means of observation, feedback, and/or explicit input, an adaptive user interface creates a model of the user, which is then used to show a user interface and/or carry out operations. When analyzing media content, such as audio and video, a content-based media processing system does so in order to provide metadata that describes the content. Using locally or remotely generated metadata—which may be automatically generated format, MPEG 7 data, an electronic programme guide, or something else entirely—a media metadata processing system processes the media in line with the metadata. Better yet, a set-top box with integrated digital rights management and digital trick play effects.
[8] Canada Patent No. CA3001304C provides a system, i.e., devices, methods, and systems for a platform for developing cyber-physical (IoT) software applications based on model-driven architecture and derived IoT SaaS applications are described herein. To obtain time-series data from time-series data sources, the system might have a time-series data component. To obtain relational data from relational data sources, the system could have a relational data component. In order to store the relational data in a relational database and the time-series data in a key-value store, the system can have a persistence component. The multi-dimensional data store, relational database, and key-value store are some examples of the data stores that the system may have. Another possibility is to create a type layer over these data stores. Depending on the number of data stores, the data services component might have definitions for several different kinds.
[9] United States Patent Application No. US9239951B2 an adaptable interface that anticipates a desired user function by taking into account the machine's internal status, context, and user history. The predictive mechanism forecasts an input and may adjust itself based on feedback. Furthermore, offered is a multimedia device pattern recognition system that matches an input to a media stream conceptually so that the device can be programmed inferentially. The system examines a stream of data to see if it matches a pattern in the data. Adaptive pattern recognition is applied to the data stream in order to extract relevant characteristics. The interface and system find use in a number of devices, such as a smart home, medical device, audio device, stocks trading terminal, environmental control system, and vehicle control system. An actuator is an optional component of the system that modifies the operating environment to enable automated learning and closed-loop feedback functioning.
[10] United States Application No. US11157926B2 reveals that predictive analytics and account management could be offered through a digital content communication system. In order to facilitate digital content management across a network, the system might have an analytics system that interfaces with one or more servers and one or more data storage. The analytics system might have an interface for data access so that it can receive customer-related data. It might also have a processor for standardizing the data that is received and processing it using a dark data processing technique. Finally, it could produce a customer fit score and a digital density score based on the dark data processing result. utilizing at least one matching technique, compare received client data to at least one variable; Make a lead analytical record (LAR), use a predictive modelling technique to rank leads in the LAR, and select the best channel based on the customer fit score, digital intensity score, LAR, matching and prioritizing activities, or at least one of these factors.
[11] Allioui, H., et al., published an article in the International Journal of Computer Engineering and Data Science (IJCEDS) entitled “Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses” (2023). Incorporating artificial intelligence (AI) has brought about improved digital services' dependability, supply chain process optimization, and instant access to priceless data and analytics. AI has the potential to revolutionize customer service standards, cut lead times, reveal new customer insights, and provide unmatched personalized experiences for businesses. In an effort to close the knowledge gap and ensure that AI is successfully included into business planning, this article aspires to greatness. It highlights the possible benefits, difficulties, and unrealized potential by performing a thorough literature study and combining modern approaches and frameworks. This study explores prospective avenues for future research as well, equipping organizations with the information and tactics they need to fully utilize AI and succeed in the fast-paced, fiercely competitive commercial environment.
[12] Aldoseri, A., et al., published an article in the international journal of Engineering, Industrial and Manufacturing Engineering entitled “A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation.” (2023). The analysis offers Automation and process optimization combined with AI-powered digital transformation has become a critical tactic for businesses looking to boost productivity, innovation, and competitiveness. This study aims to make a novel contribution by offering a thorough, well-organized roadmap that delineates the fundamental ideas required for the effective integration of automation and process optimization in the context of cutting-edge AI technology. The study presents a coherent framework made up of the following fundamental pillars: innovation, scalability, ethical and responsible AI, human-centered collaboration, seamless automation, adaptive learning and continuous improvement, data-driven insights, and strategic alignment. Within the context of AI-powered digital transformation, these pillars serve as guiding principles for navigating the complex environment of automation-driven efforts. Organizations can take a revolutionary step towards optimizing automation, promoting creativity, and establishing themselves as front-runners in the rapidly changing field of AI-driven business operations by adopting these pillars.
[13] When taken separately or in combination, none of the prior art references reveal a process that is the same as the one used in the current invention. Nanostructures had reduced activity and required lengthy formation methods in previous art. On the other hand, using AI-powered predictive business analytics, altering the tech sector and beyond in making a model in healthcare sector. In a far shorter amount of time, this invention presents a formulation that maximizes activity.
OBJECTIVES OF INVENTION
[14] The main goal of the current invention is to develop with the intention of using cutting-edge technology to significantly alter and improve a number of commercial operations' varied areas.
[15] The purpose of the current invention is to transform the way in which other sectors and the technology industry utilize AI-driven predictive business analytics, thereby promoting competitiveness, efficiency, and innovation in the quickly changing business environment. Model will help different sectors such as healthcare, financial technology, loan prediction etc. and many more sectors.
SUMMARY OF THE INVENTION
[16] The main objective is to improve predictive business analytics through the application of AI technology, with the goal of offering more insightful and accurate predictions for a range of business-related scenarios.
[17] With this technology, conventional company analytics procedures will incorporate cutting-edge AI algorithms and machine learning methodologies. The increased precision, effectiveness, and range of forecasts is the goal of this integration. The innovation improves prediction powers by examining big datasets and finding connections, patterns, and trends that would be hard for conventional analytics techniques to find.

[18] Enhancing business analytics in a particular industry is not the primary goal; changing several tech industries and beyond is also a priority. It is anticipated that the use of AI would result in major adjustments to strategic planning and decision-making procedures.

[19] Businesses may now analyses data in real-time and make timely, well-informed decisions thanks to the integration of AI. This factor is critical in industries that move quickly, where being able to react quickly to changes in the market can give an advantage over competitors.

[20] Predictive analytics driven by AI is probably scalable, and this idea ensures that the system can manage and process massive amounts of data as enterprises expand. A wide number of possible use cases outside of the tech sector are indicated by the invention, which implies that predictive analytics powered by AI can be implemented across multiple industries.

[21] The analytics system driven by AI is intended to support enterprises' strategic decision-making processes as a tool. It helps decision-makers make better decisions by providing data-driven insights.

[22] Businesses can gain a competitive edge by using AI in predictive analytics to stay ahead of market trends, optimize processes, and more quickly adjust to changes.

BRIEF DESCRIPTION OF THE DRAWINGS
[23] Results could suggest that incorporating AI into predictive business analytics improves predictions' accuracy and precision significantly. AI systems are capable of handling intricate facts and patterns, yielding more trustworthy findings.

[24] Companies may see significant improvements in their decision-making processes by utilizing AI-powered predictive analytics. Making better educated and well-planned decisions is aided by having real-time insights and a greater comprehension of market trends.

[25] It is possible that the results will demonstrate how AI-driven analytics systems can adjust to changing corporate contexts. Businesses gain a competitive edge when they can react fast to market developments through the processing and analysis of data.

[26] Research may highlight the adaptability of AI-driven predictive analytics systems, indicating its use across a range of sectors outside of the technology industry. Finance, healthcare, manufacturing, and other sectors may be included in this.

[27] Results may highlight the scalability of AI models for predictive analytics, showing that these models can manage huge datasets and develop in step with the growth of enterprises.

[28] Companies that use AI in analytics may discover that they can more successfully allocate resources for optimization. Better resource management, proactive handling of possible obstacles, and strategic planning are all part of this.

[29] Businesses may outperform their rivals if they use AI-powered predictive analytics. The findings might point to examples of innovation that achieved market leadership using AI insights-driven creativity.
[30] The study may address issues like data protection, ethical implications, and the requirement for responsible AI practices that arise when integrating AI into predictive analytics.

[31] Results may cover the significance of employee training in using AI-powered predictive analytics technologies as well as the necessity of encouraging user adoption. In many cases, the degree to which these technologies are successfully incorporated into current workflows determines their success.

DATASET LINK OF HEALTH CARE PREDICTION OF THE MODEL
[32] This is the link of healthcare prediction of the dataset as a model. https://www.kaggle.com/code/adilashrafi/diabetes-prediction-accuracy99/notebook.

DETAILED DESCRIPTION OF THE INVENTION
[33] The idea behind this invention, which has disclosed specific embodiments, is to revolutionize and optimize decision-making processes across a range of industries, particularly in the technology sector, by integrating artificial intelligence (AI) with predictive business analytics model in healthcare sector. This breakthrough promises to accelerate strategic decision-making for sustainable growth, improve operational efficiency, and offer previously unheard-of insights by utilizing the power of cutting-edge machine learning algorithms.

[34] As stated in the specification and claims, enterprises are now able to use massive amounts of data for predictive analysis thanks to the introduction of artificial intelligence. By fusing AI technologies with predictive business data, this idea aims to accelerate this shift and empower companies in the tech sector and beyond.

[35] The innovation makes use of state-of-the-art machine learning algorithms, such as reinforcement learning, deep learning, and neural networks. In order to find hidden patterns, connections, and trends, these algorithms process and analyses enormous datasets.
[36] The innovation attempts to predict future trends, market dynamics, and consumer behavior by utilizing predictive modelling techniques. Businesses may remain ahead of the competition and make well-informed decisions thanks to this.

[37] Businesses may react quickly to shifting market conditions since the system guarantees real-time data processing capabilities. This is an essential characteristic for sectors where flexibility and agility are critical. The innovation consists of easily navigable, adaptable analytics dashboards that give stakeholders clear visual representations of intricate data. This makes it easier to understand insights and encourages decision-making based on data.

[38] With its revolutionary approach to decision-making processes in the IT industry and beyond, this breakthrough represents a paradigm leap in the application of AI-powered predictive business analytics. Organizations can traverse the intricacies of the contemporary business world with unparalleled accuracy and foresight by leveraging the possibilities of advanced machine learning.

EXAMPLES
[39] The practice of the invention will be demonstrated by the following examples, some of which include preferred embodiments. It should be noted that the details provided are only meant to serve as examples and to facilitate an illustrated discussion of the invention's preferred embodiments.

Example 1: Supply Chain Optimization:
• Problem: Excess inventory or stockouts in the manufacturing sector can be caused by supply chain inefficiencies, which raises costs.
• AI-Powered Solution: In order to effectively forecast demand, predictive analytics algorithms can examine past data, industry patterns, and other pertinent aspects. As a result, businesses can decrease surplus inventory, streamline their supply chain, and increase overall productivity.
Example 2 Predictive Maintenance in Manufacturing:
• Problem: Manufacturing downtime and unforeseen equipment failures can result in significant losses.
• AI-Powered Solution: AI-powered predictive maintenance allows machinery sensors to continuously track performance. Then, using predictive analytics algorithms, equipment failure probabilities can be estimated, enabling preventive maintenance and reducing unscheduled downtime.

Example 3: Fraud Detection in Financial Services:
• Problem: Threats of fraud are ever-present in financial institutions, affecting the organization and its clients alike.
• AI-Powered Solution: In order to identify anomalies suggestive of fraud, AI systems can examine patterns in user behavior and transaction patterns. Financial organizations are able to recognize and stop fraudulent activity in real time because to this proactive strategy.
Example 4: Personalized Marketing in E-commerce:
• Problem: This has an effect on conversion rates because e-commerce platforms frequently fail to provide customers with personalized experiences.
• AI-Powered Solution: When combined with AI, predictive analytics may evaluate online activity, past purchases, and consumer preferences to make personalized product recommendations. This raises the possibility of conversion and improves the client experience overall.

Example 5: Healthcare Resource Allocation:
• Problem: The inability of hospitals and other healthcare facilities to allocate resources optimally frequently results in inefficiencies and higher expenses.
• AI-Powered Solution: To forecast the number of patients to come in, predictive analytics can examine past patient data, admission rates, and seasonal patterns. This facilitates the more effective allocation of resources by healthcare providers, guaranteeing sufficient personnel and cutting down on wasteful spending.
Example 6: Energy Grid Optimization:
• Problem: Real-time supply and demand balancing by energy providers might result in blackouts or overloads.
• AI-Powered Solution: In order to forecast energy demand, artificial intelligence (AI) systems can examine data from a variety of sources, such as past consumption statistics and weather trends. This enables energy suppliers to guarantee a steady supply of energy, optimize grid operations, and reduce outages.
Example 7: Human Resources and Talent Management:
• Problem: For many organizations, it can be difficult to find and keep outstanding personnel.
AI-Powered Solution: To find high-performing people and anticipate future attrition, predictive analytics can evaluate personnel data, performance indicators, and external market trends. This makes data-driven personnel management decisions possible for HR departments, enabling them to promote

Researchers, data scientists, and healthcare professionals interested in diabetes risk assessment and prediction in the healthcare sector will find this Diabetes Prediction Dataset to be a useful resource. This dataset includes a wide variety of carefully gathered health-related characteristics that can help in the creation of predictive models that will help identify people who are at risk of diabetes. The goal of providing this information is to encourage innovation and teamwork among data scientists, which will enhance diabetes early diagnosis and individualized treatment plans. These are the several characteristics in the medical field that help avoid diabetes.
Id: Unique identifier for each data entry.
Pregnancies: Number of times pregnant.
Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test.
BloodPressure: Diastolic blood pressure (mm Hg).
SkinThickness: Triceps skinfold thickness (mm).
Insulin: 2-Hour serum insulin (mu U/ml).
BMI: Body mass index (weight in kg / height in m^2).
DiabetesPedigreeFunction: Diabetes pedigree function, a genetic score of diabetes.
Age: Age in years.
Outcome: Binary classification indicating the presence (1) or absence (0) of diabetes.
The purpose of this dataset is to investigate the connections between different health indicators and the risk of developing diabetes. To create prediction models, feature selection plans, and data visualization tools that could lead to more precise risk assessments, machine learning approaches must be applied. Keep in mind that as you use this information, the things you learn could have a significant influence on managing and preventing diabetes.
Please make sure that you respect the privacy of the people this dataset represents and that you follow ethical principles. To encourage cooperation and information exchange, the source of this dataset should be properly cited and acknowledged.
Explore the Diabetes Prediction Dataset now to help with the ongoing efforts to fight diabetes by gaining insights from data.
Code of the model on healthcare sector
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, confusion_matrix
from sklearn.ensemble import BaggingClassifier
df=pd.read_csv("Healthcare-Diabetes.csv")
df.head()
df.info()
df.describe()
df["Outcome"].value_counts().plot(kind="bar")
df.isnull().sum()
df.duplicated().sum()
X=df.iloc[:,:-1]
y=df.iloc[:,-1]
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)
bc = BaggingClassifier(n_estimators=150, random_state=2)
bc.fit(X_train,y_train)
y_pred=bc.predict(X_test)
print(accuracy_score(y_test,y_pred))
print()
print(precision_score(y_test,y_pred))
print()
print(confusion_matrix(y_test,y_pred))
• actively address retention difficulties.

ADVANTAGES OF THE INVENTION
1. Data-Driven Decision-Making:
• Advantage: Organizations may now make decisions based on data-driven, real-time insights thanks to the integration of artificial intelligence. Because of this, decision-making becomes more deliberate and informed and depends less on gut feeling and subjective assessments.
2. Increased Operational Efficiency:
• Advantage: By pinpointing problem areas and streamlining procedures, predictive analytics simplifies operations. This leads to better resource allocation, lower costs, and higher operational efficiency.
3. Optimized Resource Management:
Advantage: Resource management can be optimized by organizations through the prediction of demand, resource requirements, and any bottlenecks. This leads to cost savings and increased productivity and includes scheduling of labor, inventory levels, and output.
4. Proactive Problem Prevention:
Advantage: Potential problems can be identified before they become more serious thanks to analytics driven by AI. This proactive strategy reduces downtime, financial losses, and other negative effects by anticipating equipment breakdowns or fraud attempts, for example.
5. Enhanced Customer Experience:
Advantage: Businesses are better able to comprehend the behavior and preferences of their customers thanks to predictive analytics. As a result, customers will ultimately have a better overall experience with personalized marketing, better product recommendations, and superior customer service.
6. Competitive Advantage:
Advantage: Businesses which use AI-powered predictive analytics to their advantage are able to anticipate client wants, keep ahead of market trends, and adjust to ever-changing business conditions. In businesses where things move quickly, this agility is essential.
7. Cost Reduction Through Efficiency:
Advantage: Costs can be decreased by using predictive analytics to find inefficiencies and wasteful spending. The optimization of inventory levels and logistics can yield substantial savings in supply chain management, making it very pertinent.
8. Improved Risk Management:
Advantage: By spotting possible threats and weaknesses, predictive analytics helps with risk assessment and management. This is advantageous in sectors like banking, where prompt risk detection can avert losses in money and harm to one's reputation.
9. Strategic Planning and Innovation:
Advantage: Strategic planning can benefit from the insightful information that AI-powered predictive analytics offers. Enterprises can leverage these discernments to detect novel prospects for their markets, develop inventive offerings, and maintain a lead in sector developments.
10. Adaptability to Market Changes:
Advantage: Organizations can respond swiftly to changes in the market thanks to the real-time processing capabilities of AI-powered analytics. This flexibility is vital in fields where success depends on staying ahead of trends.
11. Enhanced Cybersecurity:
Advantage: Artificial intelligence (AI)-powered predictive analytics improves cybersecurity in industries handling sensitive data, like finance and healthcare, by spotting and stopping possible cyber threats before they have a chance to happen.
12. Customizable and User-Friendly Analytics:
Advantage: User-friendly, configurable analytics dashboards are part of the invention. This encourages cooperation and data-driven decision-making at all levels by enabling users in all roles within an organization to access and comprehend data with ease. Because of this, the benefits of this invention go beyond specific industries and provide a thorough method for utilizing AI to boost productivity, competitiveness, and strategic decision-making in a variety of industries.

, Claims:We claim,
1. Claim: Increased Predictive Accuracy:
The model claimed in claim 1 that utilizing cutting-edge machine learning algorithms, predicted accuracy is greatly increased when AI is integrated with predictive business analytics model in healthcare sector.
2. Claim: Enhanced Operational Efficiency:
The model claimed in claim 2 that, businesses who use AI-driven predictive analytics report increased productivity in predictive business analytics model in healthcare sector.
3. Claim: Real-time Decision Support:
The model claimed in claim 3 that the survey, integrating real-time data processing capabilities enables firms to take quick decisions in predictive business analytics model in healthcare sector. Evidence shows that the system can handle and analyses data instantly, giving stakeholders timely information they need to make quick decisions.
4. Claim: Customizable Analytics Dashboards for User-Friendly Access:
The model claimed in claim 4 that, user-friendly access to information is ensured through the incorporation of configurable analytics dashboards in predictive business analytics model in healthcare sector. The fact that dashboards are made to accommodate a variety of user roles and make it simple to understand complex data serves as evidence for this.
5. Claim: Versatility Across Industries:
The model claimed in claim 5 that, predictive analytics powered by AI is not limited to the tech sector and may be applied in a variety of industries in predictive business analytics model in healthcare sector. Evidence demonstrating the system's capacity to evaluate data particular to a certain industry and customize insights to fit those needs is provided to bolster this assertion.
6. Claim: Proactive Problem Prevention Through Predictive Maintenance:
The model claimed in claim 6 that, through the use of predictive maintenance, the research asserts that the system makes proactive problem prevention possible, especially in sectors like manufacturing in predictive business analytics model in healthcare sector. Data indicating how AI systems anticipate equipment failures and minimize downtime and related losses lends credence to this.
7. Claim: Improved Customer Experience Through Personalized Marketing:
The model claimed in claim 7 that, enabling personalized marketing, AI-powered predictive analytics improves the customer experience in predictive business analytics model in healthcare sector. Evidence demonstrating how the system examines user preferences and behavior to offer customized product recommendations, ultimately increasing consumer happiness, is used to support this assertion.
8. Claim: Competitive Advantage Through Market Insight:
The model claimed in claim 8 that, staying ahead of industry trends, businesses using AI-powered predictive analytics obtain a competitive edge in predictive business analytics model in healthcare sector. There's data to back this, showing how the system can offer insightful information for innovative thinking, strategic planning, and market adaption.
9. Claim: Cost Reduction Through Efficiency Gains:
The model claimed in claim 9 that, implementing predictive analytics driven by AI reduces costs by locating and getting rid of inefficiencies in predictive business analytics model in healthcare sector. Empirical data demonstrating the system's increased operating efficiency, waste reduction, and resource management optimization provide credence to this assertion.
10. Claim: Strengthened Cybersecurity Measures:
The model claimed in claim 10 that, the technology uses predictive analytics to detect and stop possible cyber threats, according to the research, improving cybersecurity in predictive business analytics model in healthcare sector.