  Recent advancement in the field of pervasive healthcare monitoring systems
causes the generation of a huge amount of lifelog data in real-time. Chronic
diseases are one of the most serious health challenges in developing and
developed countries. According to WHO, this accounts for 73% of all deaths and
60% of the global burden of diseases. Chronic disease classification models are
now harnessing the potential of lifelog data to explore better healthcare
practices. This paper is to construct an optimal feature selection-based
unsupervised logistic regression model (OFS-ULR) to classify chronic diseases.
Since lifelog data analysis is crucial due to its sensitive nature; thus the
conventional classification models show limited performance. Therefore,
designing new classifiers for the classification of chronic diseases using
lifelog data is the need of the age. The vital part of building a good model
depends on pre-processing of the dataset, identifying important features, and
then training a learning algorithm with suitable hyper parameters for better
performance. The proposed approach improves the performance of existing methods
using a series of steps such as (i) removing redundant or invalid instances,
(ii) making the data labelled using clustering and partitioning the data into
classes, (iii) identifying the suitable subset of features by applying either
some domain knowledge or selection algorithm, (iv) hyper parameter tuning for
models to get best results, and (v) performance evaluation using Spark
streaming environment. For this purpose, two-time series datasets are used in
the experiment to compute the accuracy, recall, precision, and f1-score. The
experimental analysis proves the suitability of the proposed approach as
compared to the conventional classifiers and our newly constructed model
achieved highest accuracy and reduced training complexity among all among all.
