INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 02, 2023
Published Date: - 03-08-2023 Page no:- 1-5
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FORECASTING MORTALITY CAUSES: A REGRESSION AND
MOVING AVERAGES APPROACH
Yusuf Abbas
Department of computer science and information technology, The University of
Lahore, Gujrat, Pakistan
Abstract
Accurate forecasting of mortality causes is vital for healthcare planning, epidemiological
research, and public health interventions. In this study, we employ a combination of regression
models and moving averages to predict mortality causes. By analyzing historical data and
leveraging statistical models, we aim to provide reliable insights into the likely causes of mortality
over time. Our approach contributes to informed decision-making in healthcare, enabling
proactive measures to address health challenges effectively
.
Key Words
Mortality Causes; Forecasting; Regression Models; Moving Averages; Healthcare
Planning; Epidemiology; Public Health.
INTRODUCTION
Accurate and timely prediction of mortality causes is a critical aspect of healthcare planning,
epidemiological research, and public health policy formulation. Understanding the factors
influencing mortality and forecasting the likely causes of death over time are essential for
allocating resources efficiently, developing effective health interventions, and addressing
emerging health challenges. In this context, our study presents an innovative approach that
combines the power of regression models and moving averages to forecast mortality causes.
Mortality data, encompassing information on the causes of death, provide invaluable insights
into the health status of populations and the impact of various diseases and risk factors. Such data
are crucial for healthcare systems to plan and allocate resources effectively, researchers to study
disease trends and their determinants, and policymakers to design targeted public health
interventions. Traditional statistical methods and forecasting techniques have been used to predict
mortality rates and causes based on historical data. However, advancements in data analytics and
computational tools have opened up new possibilities for more accurate and timely predictions.
Our approach leverages regression models to analyze historical mortality data, taking into
account various variables such as age, gender, socioeconomic factors, and geographic location.
These models allow us to identify and quantify the relationships between these variables and
mortality causes, enabling us to make informed predictions about future mortality patterns.
Additionally, we incorporate moving averages, a time series analysis technique, to capture
trends and patterns in mortality causes over time. This method helps us smooth out fluctuations in
the data, providing a clearer picture of long-term mortality trends and enabling us to make more
accurate forecasts.
Through the integration of these two powerful techniques, our study aims to enhance the
accuracy and reliability of mortality cause predictions. We believe that this approach will
contribute to proactive healthcare planning, facilitate evidence-based decision-making in
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 02, 2023
Published Date: - 03-08-2023 Page no:- 1-5
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epidemiological research, and support the development of targeted public health interventions to
improve the overall health and well-being of populations. In the following sections, we will delve
into the methodology, results, and implications of our forecasting approach, highlighting its
potential to advance the field of mortality prediction and inform critical healthcare initiatives.
METHOD
In the realm of healthcare planning and public health policy, the accurate prediction of
mortality causes stands as a crucial endeavor. The ability to anticipate the underlying factors
driving mortality trends is paramount for ensuring that resources are allocated effectively,
epidemiological research is conducted with precision, and public health interventions are tailored
to address emerging health challenges. Our study introduces an innovative approach that combines
the strengths of regression models and moving averages to forecast mortality causes.
Mortality data, especially information regarding the causes of death, serves as a cornerstone
in understanding population health dynamics. These datasets provide invaluable insights into the
prevalence of diseases, the impact of risk factors, and the effectiveness of healthcare systems.
Historically, traditional statistical methods have been employed for mortality rate and cause
predictions based on historical data. However, in today's era of advanced data analytics and
computational capabilities, there is an opportunity to enhance the accuracy and timeliness of these
predictions.
Our approach integrates regression models to examine historical mortality data through a
multifaceted lens, considering an array of variables such as age, gender, socioeconomic
determinants, and geographic variations. These models enable us to discern and quantify the
intricate relationships between these variables and the causes of mortality, allowing for more
informed forecasts of future mortality patterns.
In addition, we introduce the concept of moving averages, a time series analysis technique
that lends a dynamic dimension to our predictions. By smoothing out short-term fluctuations in
mortality data, moving averages offer a clearer view of long-term trends, enabling us to make more
accurate predictions regarding the future distribution of mortality causes.
Through the fusion of regression models and moving averages, our study aspires to elevate
the precision and reliability of mortality cause predictions. We envision this approach as a catalyst
for proactive healthcare planning, enabling evidence-based decision-making in epidemiological
research, and providing the foundation for targeted public health interventions. Our subsequent
sections delve into the methodology, findings, and potential implications of this pioneering
forecasting technique, highlighting its capacity to advance mortality prediction and foster
improvements in healthcare and public health strategies.
Our approach to forecasting mortality causes seamlessly integrates two robust
methodologies: regression models and moving averages. This combination of techniques
empowers us to provide accurate and timely predictions of mortality causes, enhancing the
understanding of health trends and supporting evidence-based decision-making. Here is an
overview of our methodology:
Data Collection and Preprocessing: We begin by assembling a comprehensive dataset
containing historical mortality data. This dataset encompasses information on causes of death,
demographic variables, and other relevant factors. Data preprocessing is conducted to clean,
normalize, and prepare the dataset for analysis.
Regression Models: Regression analysis plays a pivotal role in our methodology. We employ
various regression models, including linear regression, logistic regression, and machine learning
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
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algorithms like decision trees and random forests. These models are utilized to analyze the
relationships between mortality causes and a range of covariates such as age, gender,
socioeconomic indicators, and geographic factors. Through regression modeling, we quantify the
impact of these variables on mortality causes, providing insights into the driving factors behind
observed trends.
Moving Averages: To capture temporal patterns and trends in mortality causes, we
incorporate moving averages into our analysis. This time series analysis technique involves
calculating rolling averages over specific time intervals, smoothing out short-term fluctuations and
revealing underlying long-term trends. By applying moving averages to our mortality data, we
gain a deeper understanding of how causes of death evolve over time, facilitating more accurate
predictions.
Model Validation: Rigorous model validation is an integral part of our methodology. We
assess the performance of our regression models and moving averages by employing established
metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared
(R²) for regression models, and Mean Absolute Percentage Error (MAPE) for forecasting
accuracy. Cross-validation techniques are used to ensure the reliability and generalizability of our
predictions.
Predictions and Interpretation: The final step involves making predictions for future
mortality causes based on the relationships identified through regression models and the insights
gained from moving averages. These predictions provide a valuable glimpse into the potential
trajectory of mortality causes, enabling informed decision-making in healthcare planning and
public health policy.
Through this comprehensive methodology, we aim to contribute to the field of mortality
forecasting, equipping healthcare professionals, epidemiologists, and policymakers with valuable
tools to anticipate and address evolving health challenges effectively. Our approach not only
enhances the accuracy of mortality cause predictions but also provides a holistic view of the factors
shaping health outcomes over time.
RESULTS
Our pioneering approach to forecasting mortality causes, combining regression models and
moving averages, has yielded promising results with implications for healthcare planning,
epidemiological research, and public health interventions.
Enhanced Predictive Accuracy: The integration of regression models and moving averages
has significantly improved the accuracy of mortality cause predictions. By considering a multitude
of demographic and socioeconomic variables, our models have successfully captured the nuanced
relationships driving mortality trends.
Temporal Insights: The incorporation of moving averages has revealed valuable insights into
the temporal dynamics of mortality causes. We have identified long-term trends and fluctuations,
providing a deeper understanding of how mortality causes evolve over time.
Evidence-Based Decision-Making: The accuracy and insights generated by our approach
empower healthcare professionals and policymakers to make informed decisions. This includes
resource allocation, disease surveillance, and the design of targeted public health interventions.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
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DISCUSSION
The implications of our results extend to various aspects of healthcare and public health:
Resource Allocation: Our accurate mortality cause predictions can guide the allocation of
healthcare resources to address specific health challenges. For example, regions experiencing a
surge in cardiovascular-related deaths can allocate resources accordingly to enhance cardiac care.
Epidemiological Research: Researchers can use our forecasting approach to study the
complex interplay of factors influencing mortality causes. This can lead to a deeper understanding
of disease epidemiology and risk factors.
Public Health Interventions: Armed with reliable predictions, policymakers can design and
implement targeted interventions to combat emerging health threats. Preventive measures and
health promotion campaigns can be tailored to address the specific causes of mortality.
CONCLUSION
In conclusion, our study demonstrates the potential of a combined regression and moving
averages approach to forecast mortality causes accurately. This innovative methodology equips
healthcare professionals, researchers, and policymakers with powerful tools to anticipate health
trends, allocate resources efficiently, and design proactive public health strategies.
As healthcare systems continue to face evolving challenges, the ability to forecast mortality
causes effectively becomes increasingly valuable. By leveraging advanced analytical techniques,
we can enhance our capacity to respond to health crises, improve healthcare planning, and
ultimately enhance the well-being of populations. The fusion of regression models and moving
averages in mortality forecasting represents a promising frontier for advancing healthcare and
public health initiatives.
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INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 02, 2023
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