Predicting Life Expectancy Using Machine Learning

 Thanks to the latest discoveries of data science and machine learning as we can now predict the remaining life span of a person. Our development team will guide scholars in each stage of the research process a clear-cut explanation along with various revising and editing will be provided so as to avoid errors. We tend to approach in several applications. We work out on the hidden factors about Predicting Life Expectancy and we fill in the research gaps and bring out an emerging topic to scholars by giving guidance for their entire Thesis work.

Predicting life expectancy using ML includes the understanding of several factors, involving health metrics, socioeconomic status, environmental factors, and others. In this project we can offer aspects into the decisions of lifespan and help policymakers in precedence interventions. The following is the process directives that we can implement:

1. Define the Objective:

• Based on a set of characteristics we can detect the lifetime of a person.

2. Data Collection:

• Existing Datasets: There are various datasets exists such as the World Health Organization’s data on life expectancy which we can add different parameters like GDP, education, alcohol consumption, etc.
• Collecting New Data: When we require lots of specialized local data we have to compile it from various places.

3. Data Pre-processing:

• We can handle the lost values using impute, drop and other techniques that manage them.
• Transforming the categorical variables by encrypting approaches like one-hot encoding will help us.
• Normalization and standardization of the numerical properties for the best convergence while we train our model.
• Dividing our data for the instruction, evaluation and test sets.

4. Feature Engineering:

      Here are some features and other similar things which we can use in our data:

• Health metrics: We can take the details of adult mortality, infant deaths, alcohol addict, BMI, etc.
• Socioeconomic metrics: GDP, total expenditure on health, income composition of resources and others for our project.
• Environmental factors: Some ecological factors like region (urban vs. rural), pollution indices and process to clean water will assist us.
• Disease prevalence: To detect Hepatitis B, Measles, HIV and AIDS by our work.

5. Exploratory Data Analysis (EDA):

• We observe the dispersions of features and the aiming variable.
• Searching the correlations between characteristics and life expectancy for our work.
• Utilizing the visual representations such as histograms, scatter plots and box plot can support us in the analytics.

6. Model Selection and Training:

• Initially we begin with easy structures like Linear Regression to indicate a baseline.
• We can evaluate more difficult methods such as Decision Trees, Random Forest, Gradient Boosting Machines, Neural networks.
• To refining the hyperparameters and neglect overfitting, the cross-validation techniques will help us.

7. Evaluation:

• We can measure the absolute error, mean squared fault and R-squared on the validation and test sets.
• By using using scatter plots or residual plots, we can distinguish the detected lifespan with the real values to visualize our model efficiency.

8. Deployment (Optional):

• When our focus is to offer a technique for legislators and researchers, we can apply our framework using web models such as Flask and FastAPI.
• This offers a user-friendly platform where the people can input features and get detected life expectancy by our model.

9. Feedback and Iteration:

• We fetch the review from users and shareholders.
• These feedbacks assist in improving our structure by including extra features and develop data pre-processing procedures.

Challenges:

• Causality vs. Correlation: When some characteristics correlate with life expectancy, it won’t mean they made alteration. It is very essential for us to consider the domain masters to get the outcomes appropriately.
• Data Integrity: Data from various sources can have inaccuracies and biases which will affect our project.

Extensions:

• Feature Importance Analysis: We implement methods such as SHAP (SHapley Addictive exPlanations) and permutations requirements to analyze which features are most influential in detecting life expectancy.
• Time-Series Analysis: When the data is available for many years, we can understand the life expectancy patterns and produce future predictions.

Predicting life expectancy can intense social and financial suggestions, so we often process our results with care and make sure of a throughout observations. To improve our project’s validity and effects we can consult with experts in public health, economics and other similar areas for our work. Journal Article is well written by our writers without grammatical errors according to your university style. Multiple internal reviews are conducted by the review department to avoid errors.

Predicting Life Expectancy Using Machine Learning Research Thesis Topics

Some of the very interesting research thesis topics that we have worked are explained below. Read it and get to know our work done you can also propose your own topics while we develop it in a well-mannered way by our experts.

1. An application of a supervised machine learning model for predicting life expectancy

Keywords

Life expectancy (LE), Machine learning (ML), eXtreme gradient boosting (XGBoost)

A supervised ML framework is built in this paper for predicting life expectancy rates. By utilizing XGBoost technique, we considered health, socioeconomic, and behavioral traits for prediction. This framework’s prediction is compared with other previous techniques such as Random and Artificial Neural Network regressors. As a result, XGBoost outperform other existing techniques and it is very effective in predicting life expectancy.

2. Five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms

Keywords

Prostate cancer, Five-year survival prediction, Gradient boosting, Parameters tuning, Correlation analysis

By employing ML methods, we built a model to predict a patient’s life span like whether a prostate cancer patient would survive for five years or not. When compared to the existing researches, large amount of data, correlation analysis, and unique track with hyperparameter tuning enhance the performance of our framework. Using various prediction models, patient’s five-year survival rate is examined. In that, Gradient Boosting provides effective results.

3. Determinants Factors in Predicting Life Expectancy Using Machine Learning

Keywords

Machine learning models

The major goal of this study is to find the factors that decide life expectancy. To examine the correlations among indicators, Pearson correlation coefficient is utilized. To evaluate the effect of each indicator on life expectancy, multiple linear regression models, Ridge regression, and Lasso regression are employed. As a result, multiple linear regression models are chosen for the creation of life expectancy prediction framework.

4. Research Methodology for predicting Life Expectancy using Machine Learning

Keywords

Methodology, Linear Regression, KNN

This article is a review for finding various methods and approaches to predict Life Expectancy (LE). LE considers various factors like alcohol consumption effect, death rate, life expectance in recent years and other health issues. Various ML approaches such as simple linear regression and k-nearest neighbor methods are applied to build a precise prediction model for LE. These ML methods are compared to analyze which method is precise to predict LE.

5. Machine Learning Predictive Models for Lithium-Ion Battery Life Expectancy

Keywords

SVM,RUL (Remaining Useful Life), LSTM, Lithium Ion Battery, SBM, SOH – State of Health, SOC – State of Charge, Neural Networks, prediction, Regression, Battery Life

Accurate prediction of Remaining Useful Life (RUL) for lithium-ion batteries (LIB) is very important to overcome the issue of explosion. ML approaches like LSTM (Long Short-Term Memory), SVM (Support Vector Machine), and SBM (Similarity-based Model) are employed in this paper to predict RUL in a group of LIB. In that, SBM provides better end results than other methods.

6. Life Expectancy Post Thoracic Surgery Using Machine Learning

Keywords

Thoracic surgery, Random Forest, Decision trees

The aim of this research is to suggest Life Expectancy (LE) rate and evaluate the mortality after thoracic surgery by considering important several features. Different metrics have been examined by utilizing random forest and decision tree technique to understand the outcome of post-surgery. Various classification features and health issues are analyzed like the existence of pain, hemoptysis, and cough before surgery. By doing so, LE of patients can be predicted.

7. Predicting Life Expectancy of Hepatitis B Patients using Machine Learning

Keywords

Logistic Regression (LR), Cirrhosis Mortality model (CiMM), chronic liver disease (CLD), aminotransferase (ALT), aspartate transaminase (AST), precision-recall (PR)

To find out the best model for predicting LE of Hepatitis B patient is the objective of this paper. For that, various ML methods like Logistic Regression model, Recursive Feature Elimination Algorithm, Cirrhosis Mortality model, XGBoost, Random Forest, Decision Tree are utilized by several researchers. Some techniques and methods provide promising results in prediction. As a consequence, XGBoost accomplished better outcomes.

8. Prediction of life expectancy for Asian population using machine learning algorithms

Keywords

Data Classification, Data Mining, Asian Population

Life expectancy for Asian population is proposed in this paper by employing ML techniques. Tree classifier methods such as J48, Random Tree, and Random Forest are compared. This research comprises various important factors like socioeconomic factors and educational state, health state and infectious disease for predicting LE. Results demonstrate that, Random Forest outperform other methods.

9. An IoT Framework for Healthcare Monitoring and Machine Learning for Life Expectancy Prediction

Keywords

Wireless sensor, Healthcare, Thinkspeak, Arduino

To develop a wireless healthcare framework is the goal of this study to identify patient’s health by utilizing sensors, transmits data to cloud, and employment of ML methods for LE prediction. The devices are connected in IoT environment and provide efficient healthcare service to patients. To process the data, IoT architecture collects the sensor data and transmits it to the cloud. Based on the feedback inputs and present health issues of patients, LE is predicted.

10. Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning

Keywords

MLP, Naive Bayes, NSCLC, SVM, UCI

This paper is suggested to improve life expectancy prediction accuracy by using ML techniques. The most advanced form of neural network i.e., deep neural network-based method is utilized for the prediction of post thoracic life expectancy. As a result, deep neural network achieved effective outcomes in the prediction of life expectancy.