Volume 8, Issue 4, April 2023 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165

Machine Learning Survival Analysis Model

for Diabetes Mellitus
Maureen I. Akazue1, Geofrey A. Nwokolo1, Okpako A. Ejaita2 Clement O. Ogeh1 Emmanuel Ufiofio1,
1
Department of Computer Science, Delta State University, Abraka, Nigeria
2
Department of Cyber Security, University of Delta, Agbor*

Abstract:- Developing effective survival analysis models alternate action that affects the life of patients receiving
would help guide the decision-making in managing treatment is required[8, 9 & 10].However, to respond to this
major health challenges. Model development can be need, many studies have concentrated on prediction models
achieved through various approaches. Diabetes is a in traditional techniques, but computer scientists have
health challenge in Nigeria that has attracted the interest focused on it using machine learning methodologies, to
of researchers thus much research has been carried out construct prediction models [11, 12].
as regards its management necessitating the
development of models. This study carried out a machine Machine learning has played a great role in survival
learning analysis on diabetes data collected from Central analysis helping out in clinical forecasting. Work done in
Hospital, Warri, Delta State implementing Cox-PH this area includes “machine learning for survival analysis: a
Model due to the role both play in survival analysis. A case study on recurrence of prostate cancer” [13],
dataset of 100 diabetic patients' records was collected. “ComoRbidity: an R package for the systematic analysis of
The dataset was used for training multiple machine disease comorbidities”[14], “Survival model for Diabetes
learning algorithms, namely, SupportVector (SVM), K- Mellitus Patients’ using support vector machine”[15], etc.
nearestneighbors (KNN) classifier, etc., and the proposed This field has drawn much attention in the past and has
model (Cox-PH Hybrid or CPH-SML). The performance become a dominant technology in the AI community [16].
evaluation of the machine learning algorithms and the
Survival Analysis is one of the most popular methods
proposed model gave accuracy levels as follows: KNN-
47%, SVM; 74%, and Cox-PH Hybrid-96%. The of data mining that deals withthe estimation of the time to an
event such as death, childbirth, radioactive decay, etc [3, 17,
concordance index was used to evaluate the proposed
18 & 19].
model and it had an index of 0.7204, on several
covariates such as Age, Gender, Education, Marital Due to the increasing rate of DM in the world, there
Status, history of smoking, SBP, DBP, etc. From this arises the needfor more medical attention.One area which
study's analysis of the diabetic data, it was able to has responded to such need is the area of developing
conclude that the variables associated with diabetes survival analysis models by researchers using machine
mortality are; the age of the patient and diabetes types. learning algorithms. One algorithm in Machine learning is
The patients' hazard ratio reduces when they are young the Support Vector Machine algorithm used in a recent
compared to when they are old. The patient's hazard study for survival analysis on DM patients in Nigeria
ratio is also dependent on the diabetes type. Thus, early [20].This algorithm is said to be a fault in the area of
diagnosis and proper health management of diabetics handling large datasets for analysis. This, therefore calls for
can prolong the age of diabetic patients. development of an enhanced model for diabetes survival
Keywords:- Survival Model, Machine Learning, Cox analysis. This study developed a survival analysis model
Proportional Hazard, Diabetes. using a machine learning algorithm for diabetes mellitus
patients while implementing the Cox Proportional Hazard
I. INTRODUCTION model. Dataset for diabetes data was collected from Central
Hospital in Delta State of Nigeria.
Diabetes patients are increasing at a rapid rate, and it is
estimated that more than 90-95 percent of people globally II. RELATED WORKS
have Type 2 diabetes, which is one of the leading causes of
death and contributes to a large number of deaths each year Several models have been built to solve different
in an unnoticed manner [1]. Diabetes Mellitus (DM) has human problems. These problems ranges from customers
been defined as a condition that is induced by unregulated challenges at airport, performing online transactions,
diabetes that may lead to multi-organ failure in patients [2]. advisory systems, intrusion detection systems, and so
Diabetes has become one of the biggest health challenges in onOkpeki et al.[21]; Okofu[22]; Okpeki & Omede[23];
the world hence the need to control and manage it. Okofu et al. [24]; Efozia et al. [25]; Akazue[26]; Oijoe [27];
Ojugo&Otakore [28]; Ojugo& Yoro [29].Thus, it is good to
Related works in the healthcare information systems provide survival models for health challenges. A survival
show that the increasing number of healthcare data requires model to predict the survival of pediatric Sickle Cell Disease
the need for effective means of extracting information to aid (SCD) was developed using clinical variables by Idowu et
the delivery of healthcare services to patients [3,4,5,6& 7]. al., [30]. The predictive model works with fuzzy logic.Three
The development of a prediction model to guide clinical (3) clinical variables were used and the rules for the
decisions about whether to continue therapy or take an inference engine were elicited from an expert pediatrician

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Volume 8, Issue 4, April 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
[31]. The study report on the non-validation of the fuzzy compare it to the expected life expectancyof persons living
logic-based model using live clinical datasets.Furthermore, without diabetes in the country.Analysis of confounders
relevant variables for SCD survival could have been easily wasdone by age, sex, and type of diabetes. The causes of
identified using feature selection methods from a larger mortality in diabetic patients were analyzed by making use
collection of variables monitored for pediatric SCD survival of Kaplan-Meier survival curves for each age cluster. Lon-
as observed, but the study found that the researchers did rank analysis was not also left out. Findings in the study
simulate nor validate the rule-base of the classification include; male diabetics patients showing slightly longer life
model for SCD survival [32]. expectancy than their counterparts in the non-diabetic
population, by a marginal gain of 0.6 years for the entire
Machine learning was applied to the prediction of the observed period. Furthermore, life expectancy indiabetic
survival of pediatricHIV/AIDS patients [33]. The naïve women was said to increaseby 1.3 years, which was not
Bayes’ classifier was employed. The model was trained, observed in the non-diabetic population. Diabetes was said
evaluated, and gave a result that showed that the classifier to occur more in women.
was able to predict the survival of HIV/AIDS patients by the
model's accuracy of 68%. The Support Vector Machine was used by Bamidele et
al., [38] to develop a survival model for diabetes mellitus.
A pilot study on type 2 diabetes patients, showed the Identification of variables monitored during the
use of Deep learning to develop a novel adherence management of diabetes mellitus patients was done.
detection. This was based on simulated Continuous Glucose
Monitoring (CGM) signals. CGM signals were simulated in Lee et al., [39] did work in Hong Kong with the aim of
a large and diverse amount for T2D patients with the aid of developing a predictive risk modelfor all-cause mortality in
a T2D-adapted version of the Medtronic Virtual Patient patients with diabetes. The study used a multi parametric
(MVP) model for T1D [34]. Due to the signals, different approach with data from different domains. The association
classification algorithms were compared using a of risk variables and all-causemortality was assessed using
comprehensive grid search. The researchers for the study Cox proportionalhazardsmodels. Machine learning
contrasted a standard logistic regression baseline to Multi- approaches were used in the study to improve overall
Layer Perceptrons (MLPs) and Convolutional Neural survival prediction and were evaluatedwith a fivefold cross-
Networks (CNNs). It was reported that the best validation method. Age, male gender, baseline
classification performance having an average accuracy of comorbidities, etc were significant predictors of all-cause
77:5% came as a result of employing CNN. This study, mortality found through Multivariate Cox regression. The
therefore, confirms the potential of Deep Learning as study affirms that a multi parametric model incorporating
regards adherence detection systems for Type 2 disease variables from different domains predicted all-cause
patients [34]. mortality accurately in type 2 diabetes mellitus.

Predicting factors for the survival of breast cancer Survival analysis has also been done on coronavirus
patients using machine learning techniques was done in patients with the introduction of two models called
2019. The study developed models for detecting and Cox_COVID_19 and Deep_ _Cox_COVID_19. These
visualizing significant prognostic indicators of breast cancer models were developed to help hospitals select patients with
survival rate [35]. The datasets were a hospital-based breast better chances of survival and to predict the most important
cancer dataset from the University of Malaya Medical features affecting the rate of survival [39]. One of the
Centre, Kuala in Malaysia with diagnostic information survival models; Cox_COVID_19 is based on Cox
between 1993 and 2016. To determine the predicting regression while the second model; Deep_Cox_COVID_19
factors, models were developed with a decision tree, random is a hybrid model, i.e a combination of autoencoder deep
forest, neural networks, extreme boots, logistic regression, neural network and Cox regression. The study affirms that
and support vector machine. The study affirms that all both systems (i.e the Cox_COVID_19 and Deep_
algorithms produced close outcomes, with the lowest Cox_COVID_19) can predict the survival likelihoodand
obtained from the decision tree (accuracy = 79.8%) and the also present significant symptoms that differentiate severe
highest from the random forest (accuracy = 82.7%). cases and death cases.

More work done on breast cancer survival Ojie, et al., [41] applied classification algorithm in
includesKalafi et al., [36] using 4,902 patient records from their proposed hybrid model of Genetic Algorithm and Data
the University of Malaya Medical Centre Breast Cancer value Metric (DVM) as an information theoretic metric for
Registry (UMMCBCR).The prediction modelswere quantifying the quality and utility for feature selection. They
designed and implemented by machine learning(SVM, RF, proposed that this can be applied to traditional data.
and DT) and deep learning MLP techniques.Findings show
that the multilayer perceptron (MLP),random forest (RF)
and decision tree (DT) classifierscould predict survivorship,
respectively, with 88.2 %, 83.3 %, and 82.5 % accuracy in
the tested samples. And Support vector machine (SVM) was
recorded lower with 80.5 %.
Tachkov et al.,[37] conducted a study to evaluate the III. METHODS AND MATERIALS
expected life expectancy in patients with diabetes and to

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Volume 8, Issue 4, April 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
To carry out survival analysis and develop a predictive The diabetes data were recorded using a spreadsheet with
model on diabetes mellitus using a machine learning the assistance of the health workers in the unit. Most of the
algorithm, the following steps were adopted (i) Data features relating to diabetes that needed to be collected for
collection, (ii) Data preparation, (iii) Implementing the the study were outlined by medical personnel that the
proposed model, and (iv)Evaluation. researcher contacted.
A. Data Collection
After ethical approval, the researcher collected various Features relating to the survival or mortality of
variables from the records of 100 diabetes patients from the diabetes mellitus patients were collected from the Hospital.
state central Hospital, Warri-Delta State following due Table 1 below is a description of the variables collected and
procedures for collecting patient data in the health facility. used for the proposed system.

Table 1: Identified Variables for determining Diabetes Mellitus

S/N Names of Variables Labels
1 Gender Male, Female
2 Present Age (in years) Numeric
3 Highest Education Primary, Secondary, Tertiary, Others, Nil
4 Occupation Business, Civil Servant, Teacher, Electrician, Trader, Carpenter, Farmer, Cleaner, Nil
5 Marital Status Single, Married, Widow, Widower, Divorced
6 Ethnicity Urhuoba, Yoruba, Igbo, Hausa, Itsekiri, Ijaw
7 History of Smoking Yes, No
8 Diabetes Type Type 1, Type 2, Gestational
9 Age at Diagnosis (in years) Numeric
10 Systolic Blood Pressure (SPB) Numeric
11 Diastolic Blood Pressure (DPB) Numeric
12 BMI Class Underweight, Normal, Overweight, Obese
13 Haemoglobin A1c Numeric
14 Treatment Plan Insulin, others
15 History of Drug Resistance Yes, No
16 Complication Neuropathy, Nephropathy, Retinopathy, CVD, Stroke, Peripheral Artery Disease
17 Mortality Numeric

B. Data Preparation IV. IMPLEMENTING THE PROPOSED MODEL

After the collection of data, the data was analyzed and
cleaned. This means that data necessary for use in the A. Data Training and Learning
various machine-learning algorithms were processed. The The datasets are prepared for training and learning the
process involved the following: features of the datasets using the various machine learning
 The ethnicity (Tribe) column was dropped. methods, including Random Forest, Gradient Boosting,
 Yes / No type of columns were respectively converted and Decision Tree, Support Vector, Multi-Layer Perceptron, and
cleaned. K-nearest Neighbors' classifier. During the training process,
 Single observation on target variable value was found to the model makes an effort to comprehend the properties and
be missing and hence dropped. instance representation of a certain dataset that is used as
 Missing observations on a few categorical columns were input. The text input is converted by the feature extractor
into a feature vector that categorizes its polarity. The
detected and thus filled with their respective column mode
programmer chose to divide the datasets into two halves for
value.
this investigation, with the ratio being 80% to 20% for
 All the categorical features were one-hot encoded.
training and learning, respectively. The training data is
 All the values in the dataset were scaled between 0 and 1 represented using the attributes after the instance and
as a standardization technique. attributes have been chosen.
 Dataset was split into training and testing sets ina ratio of
80:20. B. Data Testing and Classification
 An extreme imbalance was detected on the target The output of the training datasets was compared with
variablein the ratio of 85:14. It was handled by that of the testing datasets in order to check all conceivable
applyingSynthetic Minority Oversampling Technique combinations and evaluate how effectively a model will
(SMOTE). predict the intended or expected results. Were the expected
result far different from the output result, the input was
The stepwise variable feature selection with iteration adjusted and the model was fine-tuned based on the results
between the ‘forward and backward’ steps to obtain the best of the test data set. This was accomplished by comparing the
patient final Cox proportional hazard model was then attributes of the training and testing datasets, computing the
implemented, tested, and evaluated for optimum probability for each hypothesis based on the attributes, and
performance. categorizing the attributes that were most similar to the
outcome. Then Real-world datasets were then fed into the
classifier for the categorization of tweets and spam emails

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Volume 8, Issue 4, April 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
while taking into account all of the classifying methods algorithms. Then, a model with Random Forest Classifier
either as a single selection or bulk selection. First, the and Cox Proportional Hazard algorithm was then developed
classifier is fed with testing datasets to check the correctness with the title CPH-MLS Prediction model and deployed to
of the algorithm. the web app with a model performance accuracy of 96%.

C. Building the Model From the fitted COX PH Model the variables that are
Multiple models were trained, namely, Random Forest, associated with the mortality are the age of the patient,
Gradient Boosting, DecisionTree, Support Vector, Multi- diabetes, and education level. The patients' hazard ratio
Layer Perception, and K-nearest neighbors' classifier. The increases by 1.95 when they have secondary education
Cox proportionality Hazards model was also implemented compared to when they have primary education. The
and evaluated on the dataset. This model was introduced by patients' hazard ratio reduces by 0.96 when they are young
Cox and takes into account the effect of several variables at compared to when they are old. The patients' hazard ratio
a time and the relationship of their survival distribution to reduces by 0.53 when they have diabetes compared to when
these variables. The Random Forest algorithm yielded the they have no diabetes.
best result with an accuracy of 82% against other

Fig. 1: Graph of Model prediction

The above chart shows the usability of the model in D. Evaluation

predicting the probability of having diabetes or not and the The performance metrics were used to evaluate the
survival time of new patients. Thus the chart gives the performance of the various algorithms in this study. This
survival time and probability. On the left is the prediction, 0, gives the overall performance summary of the learning
1. When the chart reads 1, it means that it is predicting algorithms on the datasets and weighted (average)
possible cases of diabetes mortality for the patient record performance rate which is shown below in table 2, fig 2, and
analyzed and when it reads 0, it indicates no diabetes fig 3.
mortality prediction.

Table 2: Summary of Model Performance

Model Precision Recall F1-Score Accuracy
0.45 0.56 0.5 47%
KNN
0.5 0.4 0.44
0.62 0.56 0.59 63%
Logistic
0.64 0.7 0.67
1 0.44 0.62 74%
SVM
0.67 1 0.8
0.93 1 0.96 96%
Cox-PH Hybrid
0.96 0.96 0.96

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Volume 8, Issue 4, April 2023 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165

ACCURACY CHART
100

80
KNN
60
Logistic
40 SVM

20 Cox-PH Hybrid

0
Accuracy

Fig. 2: Accuracy Performance Analysis on Diabetes Datasets

0.98
1
0.9
0.8 0.72
0.7 0.63
Precision
0.6
0.48
0.5 Recall
0.4
F1-Score
0.3
0.2
0.1
0
KNN Logistic SVM Cox-PH Hybrid
Fig. 3: Precision, Recall Value, and F-Measure Analysis on Diabetes Datasets

V. RESULT AND DISCUSSION The patient’s hazard ratio reduces by 0.53 when they have
diabetes compared to when they have no diabetes.
To perform the survival analysis of diabetes mellitus,
this study has developed a hybrid model of implementing VI. CONCLUSION
the Random Forest algorithm Cox-PH. From the
performance and evaluation of the designed model, this The study has been able to establish that a machine
study has shown that the integration of Machine Learning learning model for survival analysis and prediction can be
and the Cox-proportional hazard model in survival analysis implemented along parameter or non-parameter tools for
is achievable. The result of the model on the dataset showed modeling time-to-event data. This is because machine
that the variables that are associated with diabetes mortality learning tools and algorithms and efficient in building
are; the age of the patient and diabetes types. The study prediction and analysis models.
shows that patients' hazard ratio reduces by 0.96 when they
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