Automated Machine Learning Tools Research Articles

A Comparative Analysis of Automated Machine Learning Tools: A Use Case for Autism Spectrum Disorder Detection

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families.

Gendered Extremism in the Pacific on 4chan: A Mixed-methods Exploration of Australian and New Zealanders’ Concepts of Women, Gender, and Sexual Violence on /Pol/

ABSTRACT The association between 4chan and online extremist subcultures has seen increasing academic scrutiny—particularly following the 2019 Christchurch attack by a right-wing terrorist who frequented the anonymous forums. Gender-based extremism features as one (of many) critical subcultures that commands our academic attention, though few studies to date have sought to capture and assess the entire landscape of this phenomenon on 4chan’s most notorious board: /pol/. Drawing on a pre-Christchurch attack dataset extracted from Papasavva et al. (2020), this study investigates how Australians and New Zealanders (ANZ) broadly conceptualize—and debate—women, gender, and sexual violence on 4chan’s /pol/ board. We apply a mixed-methods approach, combining automated machine learning tools alongside expert qualitative analysis. Across nearly 300,000 posts and comments, we show how gender is constructed within this community, and the conjugal order they demand as a result. This order racially and sexually defines gender identities and norms, which are perceived as mechanisms to restore power and dominance to an ethnically and ideologically conforming in-group. Those that violate or disrupt the conjugal order are legitimized as targets of sexual, and gender-based violence. This normalizes far/extreme right gendered constructs across ANZ contexts in support of exclusivist far/extreme right ideological positions.

PyCaret for Predicting Type 2 Diabetes: A Phenotype- and Gender-Based Approach with the "Nurses' Health Study" and the "Health Professionals' Follow-Up Study" Datasets.

Predicting type 2 diabetes mellitus (T2DM) by using phenotypic data with machine learning (ML) techniques has received significant attention in recent years. PyCaret, a low-code automated ML tool that enables the simultaneous application of 16 different algorithms, was used to predict T2DM by using phenotypic variables from the "Nurses' Health Study" and "Health Professionals' Follow-up Study" datasets. Ridge Classifier, Linear Discriminant Analysis, and Logistic Regression (LR) were the best-performing models for the male-only data subset. For the female-only data subset, LR, Gradient Boosting Classifier, and CatBoost Classifier were the strongest models. The AUC, accuracy, and precision were approximately 0.77, 0.70, and 0.70 for males and 0.79, 0.70, and 0.71 for females, respectively. The feature importance plot showed that family history of diabetes (famdb), never having smoked, and high blood pressure (hbp) were the most influential features in females, while famdb, hbp, and currently being a smoker were the major variables in males. In conclusion, PyCaret was used successfully for the prediction of T2DM by simplifying complex ML tasks. Gender differences are important to consider for T2DM prediction. Despite this comprehensive ML tool, phenotypic variables alone may not be sufficient for early T2DM prediction; genotypic variables could also be used in combination for future studies.

Enhancing urban cycling safety: The role of automated helmet compliance monitoring with EasyDL

With an increasing emphasis on individual safety in urban transportation, our study addresses the critical issue of bicycle helmet compliance. Helmets play an essential role in preventing cycling-related injuries, yet their usage often falls short of safety standards. We utilize Baidus EasyDL, an automated machine learning tool, to detect the use of bicycle helmets in varied urban environments. This approach leverages a large, annotated dataset dedicated to bicycle helmets, achieving high precision in identifying helmet usage. Our preliminary tests in simulated urban settings show the methods capability to enhance safety measures effectively. This tool aids in real-time monitoring and pinpointing areas where helmet usage is neglected, aiming to elevate safety consciousness among cyclists. The study demonstrates the practicality of using automated helmet detection to improve urban cycling safety, providing a user-friendly option for those without a deep programming background. Our work underscores the value of EasyDL in analyzing bicycle helmet datasets and its potential role in reinforcing essential safety practices in urban cycling scenarios.

MSR63 Improving Efficiency in Analysis of Real-World Data with an Automated Machine Learning Tool

MSR63 Improving Efficiency in Analysis of Real-World Data with an Automated Machine Learning Tool

Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises

Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

Machine learning-based clinical decision support using laboratory data.

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Machine-learning-aided density functional theory calculations of stacking fault energies in steel

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

J-PLUS: galaxy-star-quasar classification for DR3

ABSTRACT The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources. J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the data set, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars, and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called TPOT to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR18, LAMOST DR8, and Gaia. We checked that the training set of about 660 thousand galaxies, 1.2 million stars, and 270 thousand quasars is both representative and contain a minimal presence of contaminants (less than 1 per cent). We considered 37 features: the 12 photometric bands with respective errors, 6 colours, 4 morphological parameters, galactic extinction with its error, and the PSF relative to the corresponding pointing. With TPOT genetic algorithm, we found that XGBoost provides the best performance: the AUC for galaxies, stars, and quasars is above 0.99 and the average precision is above 0.99 for galaxies and stars and 0.96 for quasars. XGBoost outperforms the classifiers already provided in J-PLUS DR3 and also classifies quasars.

Aliro: an automated machine learning tool leveraging large language models.

Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training. Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data. Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.

BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designsoptimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry

Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions.

PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.

There is evidence that non-coding RNAs play significant roles in the regulation of nutrient homeostasis, development, and stress responses in plants. Accurate identification of ncRNAs is the first step in determining their function. While a number of machine learning tools have been developed for ncRNA identification, no dedicated tool has been developed for ncRNA identification in plants. Here, an automated machine learning tool, PINC is presented to identify ncRNAs in plants using RNA sequences. First, we extracted 91 features from the sequence. Second, we combined the F-test and variance threshold for feature selection to find 10 features. The AutoGluon framework was used to train models for robust identification of non-coding RNAs from datasets constructed for four plant species. Last, these processes were combined into a tool, called PINC, for the identification of plant ncRNAs, which was validated on nine independent test sets, and the accuracy of PINC ranged from 92.74% to 96.42%. As compared with CPC2, CPAT, CPPred, and CNIT, PINC outperformed the other tools in at least five of the eight evaluation indicators. PINC is expected to contribute to identifying and annotating novel ncRNAs in plants.

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.

BackgroundUnfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required.ObjectiveIn this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH.MethodsThis was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy.ResultsIn predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model.ConclusionsTo the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.

Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data

Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial big data” can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes.

An automated machine learning tool for breast cancer diagnosis for healthcare professionals

ABSTRACT The paper proposes a hybrid metaheuristic algorithm known as harmony search and simulated annealing (HS-SA) for accurate and precise breast malignancy disclosure by integrating harmony search (HS) and simulated annealing (SA) optimisation methods. An enhanced wavelet-based contourlet transform (WBCT) procedure for mining the highlights of the region of interest (ROI) is explored, that allows execution upgradation over other standard procedures. The anticipated HS-SA algorithm aims to reduce the feature dimensions and assemble at the unparalleled optimal feature subset. The SVM classifier fed with the picke.d feature subsets and assisted by varied kernel functions upheld its classification capacities in contrast with the conformist machine learning classification and optimisation methods. The portrayed computer-aided diagnosis (CAD) model is confronted by evaluating its learning capability on two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Preliminary propagations, experimental outcomes, and quantifiable assessments likewise demonstrate that the proposed model is pragmatic and favourable for the automated breast malignancy findings with optimal performance and fewer overheads. The discoveries show that the proposed CAD system (HS-SA+Kernel SVM) is superior to various characterisation accuracy techniques with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset while keeping the element space restricted to only seven feature subsets and computational prerequisites as low as is judicious.

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

BackgroundIn epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large.ObjectiveOur study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis.MethodsAn XGBoost model was trained and tested to classify “current depression” and “no lifetime depression” for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network.ResultsThe XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (P<.05) and indirect (P≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes.ConclusionsXGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.

Using a Simple Open-Source Automated Machine Learning Algorithm to Forecast COVID-19 Spread: A Modelling Study

Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics. Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea's centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric. As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period. Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

MLsurvival: an automated machine learning tool for survival analysis of cancer patients.

e13555 Background: Survival analysis is used to establish a connection between covariates and the time of event with censored data. Compared with traditional statistical methods, machine learning approaches based on sophisticated and effective computational algorithms are more capable for handling complex multi-dimensional medical data. Methods: We developed an automated machine learning tool MLsurvival to analyze survival data of cancer patients, algorithms of which include the statistical cox regression and machine learning based on linear model (elastic net), ensemble model (gradient boosting with least squares or regression trees and random forest) and support vector kernel (linear and non-linear). The workflow of MLsurvival is comprised with four modules: preprocessing (missing data remove or imputation and feature standardization), feature selection (unsupervised multi-statistics and supervised machine recursive feature elimination with cross-validation), modeling (hyperparameter and performance evaluation) and prediction. To evaluate the performance of this tool, we analyzed medical data for 222 hepatocellular carcinoma (HCC) patients at stage II-III who underwent surgical resection and developed five machine learning approach based estimation models for overall survival (OS). Models were trained on 155 patients with 300 features, including clinical information, somatic mutation and copy number variation, and independently validated on the rest 67 patients. Results: The ensemble model of gradient boosting fitted by MLsurvival using 48 selected features for the data of 155 HCC patients possessed the highest mean AUC and C-Index value. For 67 patients in validation set, this model predicted half year mortality of patients with an AUC of 0.9 (95% CI, 0.771-1.029) and one year mortality with an AUC of 0.897 (95% CI, 0.816-0.978). In addition to that, this model was also predictive for the time of recurrence (pvalue &lt; 0.0001). Furthermore, we also utilized this tool in survival analysis for extensive real data from patients with breast, lung, and esophagus cancers, while most of results showed superior accuracy and stable performance. Conclusions: MLsurvial is an automate tool for survival analysis of cancer patients with well performance. The risk scoring system implemented in this tool offers a novel strategy for incorporating multi-dimensional risk factors to predict clinical outcome, contributes to the better understanding of disease background and helps to optimize the clinical follow-up and therapeutic treatment for cancer patients.